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Data obtained with the use of massive parallel sequencing (MPS) can be valuable in population genetics studies. In particular, such data harbor the potential for distinguishing samples from different populations, especially from those coming from adjacent populations of common origin. Machine learning (ML) techniques seem to be especially well suited for analyzing large datasets obtained using MPS. The Slavic populations constitute about a third of the population of Europe and inhabit a large area of the continent, while being relatively closely related in population genetics terms. In this proof-of-concept study, various ML techniques were used to classify DNA samples from Slavic and non-Slavic individuals. The primary objective of this study was to empirically evaluate the feasibility of discerning the genetic provenance of individuals of Slavic descent who exhibit genetic similarity, with the overarching goal of categorizing DNA specimens derived from diverse Slavic population representatives. Raw sequencing data were pre-processed, to obtain a 1200 character-long binary vector. A total of three classifiers were used—Random Forest, Support Vector Machine (SVM), and XGBoost. The most-promising results were obtained using SVM with a linear kernel, with 99.9% accuracy and F1-scores of 0.9846–1.000 for all classes.

The phenotype is the result of the interaction of the genotype with epigenetic factors and the environment. It has been considered that genetics is responsible for >70% of facial phenotype. Interestingly, besides sequencing nucleic acids, it is possible to generate DNA methylation maps of ancient remains, to determine phenotypes. Other application areas are medicine, forensics and law-enforcement. Thus, breakthroughs in nucleic-acid analyses are allowing to determine the phenotype from genotypic data. That is also being facilitated by developments in software (including parallel processing) and hardware, including neural engines (neural-network hardware). New strategies involving artificial intelligence and machine learning have been also deployed to reach such a goal. Phenotyping is a challenging task, on the edge of current technology. Promising results have already been obtained, including prediction of Neanderthal (Homo sapiens neanderthalensis) and Denisovan (Homo sapiens denisova) faces. The future is promising in this research area, in which ethical and legal implications should also be considered., El fenotipo es el resultado de la interacción del genotipo con factores epigenéticos y el medio ambiente. Se ha considerado que la genética es responsable de >70% del fenotipo facial. Curiosamente, además de secuenciar ácidos nucleicos, es posible generar mapas de metilación de ADN de restos antiguos, para determinar fenotipos. Otras áreas de aplicación son la medicina, ciencia forense y control del cumplimiento de la ley. Así, los avances en el estudio de ácidos nucleicos están permitiendo determinar el fenotipo a partir de datos genotípicos. Ello también se ve facilitado por los desarrollos en software (incluido el procesamiento paralelo) y hardware, incluidos los motores neuronales (hardware de redes neuronales). También se han implementado nuevas estrategias, involucrando inteligencia artificial y aprendizaje automático, para alcanzar dicho objetivo. El fenotipado es una tarea desafiante, a la vanguardia de la tecnología actual. Ya se han obtenido resultados prometedores, incluida la predicción de caras de neandertales (Homo sapiens neanderthalensis) y denisovanos (Homo sapiens denisova). El futuro es prometedor en esta área de investigación, en la que también se deben considerar las implicaciones éticas y legales.

The phenotype is the result of the interaction of the genotype with epigenetic factors and the environment. It has been considered that genetics is responsible for >70% of facial phenotype. Interestingly, besides sequencing nucleic acids, it is possible to generate DNA methylation maps of ancient remains, to determine phenotypes. Other application areas are medicine, forensics and law-enforcement. Thus, breakthroughs in nucleic-acid analyses are allowing to determine the phenotype from genotypic data. That is also being facilitated by developments in software (including parallel processing) and hardware, including neural engines (neural-network hardware). New strategies involving artificial intelligence and machine learning have been also deployed to reach such a goal. Phenotyping is a challenging task, on the edge of current technology. Promising results have already been obtained, including prediction of Neanderthal (Homo sapiens neanderthalensis) and Denisovan (Homo sapiens denisova) faces. The future is promising in this research area, in which ethical and legal implications should also be considered., El fenotipo es el resultado de la interacción del genotipo con factores epigenéticos y el medio ambiente. Se ha considerado que la genética es responsable de >70% del fenotipo facial. Curiosamente, además de secuenciar ácidos nucleicos, es posible generar mapas de metilación de ADN de restos antiguos, para determinar fenotipos. Otras áreas de aplicación son la medicina, ciencia forense y control del cumplimiento de la ley. Así, los avances en el estudio de ácidos nucleicos están permitiendo determinar el fenotipo a partir de datos genotípicos. Ello también se ve facilitado por los desarrollos en software (incluido el procesamiento paralelo) y hardware, incluidos los motores neuronales (hardware de redes neuronales). También se han implementado nuevas estrategias, involucrando inteligencia artificial y aprendizaje automático, para alcanzar dicho objetivo. El fenotipado es una tarea desafiante, a la vanguardia de la tecnología actual. Ya se han obtenido resultados prometedores, incluida la predicción de caras de neandertales (Homo sapiens neanderthalensis) y denisovanos (Homo sapiens denisova). El futuro es prometedor en esta área de investigación, en la que también se deben considerar las implicaciones éticas y legales.

The phenotype is the result of the interaction of the genotype with epigenetic factors and the environment. It has been considered that genetics is responsible for >70% of facial phenotype. Interestingly, besides sequencing nucleic acids, it is possible to generate DNA methylation maps of ancient remains, to determine phenotypes. Other application areas are medicine, forensics and law-enforcement. Thus, breakthroughs in nucleic-acid analyses are allowing to determine the phenotype from genotypic data. That is also being facilitated by developments in software (including parallel processing) and hardware, including neural engines (neural-network hardware). New strategies involving artificial intelligence and machine learning have been also deployed to reach such a goal. Phenotyping is a challenging task, on the edge of current technology. Promising results have already been obtained, including prediction of Neanderthal (Homo sapiens neanderthalensis) and Denisovan (Homo sapiens denisova) faces. The future is promising in this research area, in which ethical and legal implications should also be considered., El fenotipo es el resultado de la interacción del genotipo con factores epigenéticos y el medio ambiente. Se ha considerado que la genética es responsable de >70% del fenotipo facial. Curiosamente, además de secuenciar ácidos nucleicos, es posible generar mapas de metilación de ADN de restos antiguos, para determinar fenotipos. Otras áreas de aplicación son la medicina, ciencia forense y control del cumplimiento de la ley. Así, los avances en el estudio de ácidos nucleicos están permitiendo determinar el fenotipo a partir de datos genotípicos. Ello también se ve facilitado por los desarrollos en software (incluido el procesamiento paralelo) y hardware, incluidos los motores neuronales (hardware de redes neuronales). También se han implementado nuevas estrategias, involucrando inteligencia artificial y aprendizaje automático, para alcanzar dicho objetivo. El fenotipado es una tarea desafiante, a la vanguardia de la tecnología actual. Ya se han obtenido resultados prometedores, incluida la predicción de caras de neandertales (Homo sapiens neanderthalensis) y denisovanos (Homo sapiens denisova). El futuro es prometedor en esta área de investigación, en la que también se deben considerar las implicaciones éticas y legales.

The phenotype is the result of the interaction of the genotype with epigenetic factors and the environment. It has been considered that genetics is responsible for >70% of facial phenotype. Interestingly, besides sequencing nucleic acids, it is possible to generate DNA methylation maps of ancient remains, to determine phenotypes. Other application areas are medicine, forensics and law-enforcement. Thus, breakthroughs in nucleic-acid analyses are allowing to determine the phenotype from genotypic data. That is also being facilitated by developments in software (including parallel processing) and hardware, including neural engines (neural-network hardware). New strategies involving artificial intelligence and machine learning have been also deployed to reach such a goal. Phenotyping is a challenging task, on the edge of current technology. Promising results have already been obtained, including prediction of Neanderthal (Homo sapiens neanderthalensis) and Denisovan (Homo sapiens denisova) faces. The future is promising in this research area, in which ethical and legal implications should also be considered., El fenotipo es el resultado de la interacción del genotipo con factores epigenéticos y el medio ambiente. Se ha considerado que la genética es responsable de >70% del fenotipo facial. Curiosamente, además de secuenciar ácidos nucleicos, es posible generar mapas de metilación de ADN de restos antiguos, para determinar fenotipos. Otras áreas de aplicación son la medicina, ciencia forense y control del cumplimiento de la ley. Así, los avances en el estudio de ácidos nucleicos están permitiendo determinar el fenotipo a partir de datos genotípicos. Ello también se ve facilitado por los desarrollos en software (incluido el procesamiento paralelo) y hardware, incluidos los motores neuronales (hardware de redes neuronales). También se han implementado nuevas estrategias, involucrando inteligencia artificial y aprendizaje automático, para alcanzar dicho objetivo. El fenotipado es una tarea desafiante, a la vanguardia de la tecnología actual. Ya se han obtenido resultados prometedores, incluida la predicción de caras de neandertales (Homo sapiens neanderthalensis) y denisovanos (Homo sapiens denisova). El futuro es prometedor en esta área de investigación, en la que también se deben considerar las implicaciones éticas y legales.

Skin pigmentation is one of the most prominent and variable phenotypes in humans. We compared the alleles of 163 SNPs and indels from the Human Pigmentation (HuPi) AmpliSeq™ Custom panel, and biogeographic ancestry with the quantitative skin pigmentation levels on the upper arm, lower arm, and forehead of 299 Pakistani individuals from three subpopulations: Baloch, Pashtun, and Punjabi. The biogeographic ancestry of each individual was estimated using the Precision ID Ancestry Panel. All individuals were mainly of mixed South-Central Asian and European ancestry. However, the Baloch individuals also had an average proportion of Sub-Saharan African ancestry of approximately 10%, whereas it was <1% in the Punjabi and Pashtun individuals. The pairwise genetic distances between the Pashtun, Punjabi, and Baloch subpopulations based on the ancestry markers were statistically significantly different. Individuals from the Pashtun subpopula-tion had statistically significantly lower skin pigmentation than individuals from the Punjabi and Baloch subpopulations (p < 0.05). The proportions of European and Sub-Saharan African ancestry and five SNPs (rs1042602, rs10831496, rs1426654, rs16891982, and rs12913832) were statistically significantly associated with skin pigmentation at either the upper arm, lower arm or forehead in the Pakistani population after correction for multiple testing (p < 10−3). A model based on four of these SNPs (rs1426654, rs1042602, rs16891982, and rs12913832) explained 33% of the upper arm skin pigmentation. The four SNPs and the proportions of European and Sub-Saharan African ancestry explained 37% of the upper arm skin pigmentation. Our results indicate that the four likely causative SNPs, rs1426654, rs1042602, rs16891982, and rs12913832 located in SLC24A5, TYR, SLC45A2, and HERC2, respectively, are essential for skin color variation in the admixed Pakistani subpopulations.

Skin pigmentation is one of the most prominent and variable phenotypes in humans. We compared the alleles of 163 SNPs and indels from the Human Pigmentation (HuPi) AmpliSeq™ Custom panel, and biogeographic ancestry with the quantitative skin pigmentation levels on the upper arm, lower arm, and forehead of 299 Pakistani individuals from three subpopulations: Baloch, Pashtun, and Punjabi. The biogeographic ancestry of each individual was estimated using the Precision ID Ancestry Panel. All individuals were mainly of mixed South-Central Asian and European ancestry. However, the Baloch individuals also had an average proportion of Sub-Saharan African ancestry of approximately 10%, whereas it was <1% in the Punjabi and Pashtun individuals. The pairwise genetic distances between the Pashtun, Punjabi, and Baloch subpopulations based on the ancestry markers were statistically significantly different. Individuals from the Pashtun subpopula-tion had statistically significantly lower skin pigmentation than individuals from the Punjabi and Baloch subpopulations (p < 0.05). The proportions of European and Sub-Saharan African ancestry and five SNPs (rs1042602, rs10831496, rs1426654, rs16891982, and rs12913832) were statistically significantly associated with skin pigmentation at either the upper arm, lower arm or forehead in the Pakistani population after correction for multiple testing (p < 10−3). A model based on four of these SNPs (rs1426654, rs1042602, rs16891982, and rs12913832) explained 33% of the upper arm skin pigmentation. The four SNPs and the proportions of European and Sub-Saharan African ancestry explained 37% of the upper arm skin pigmentation. Our results indicate that the four likely causative SNPs, rs1426654, rs1042602, rs16891982, and rs12913832 located in SLC24A5, TYR, SLC45A2, and HERC2, respectively, are essential for skin color variation in the admixed Pakistani subpopulations.