Abstract:
Natural selection is a mechanism of evolution, and genetic mutations form the basis of this evolutionary mechanism. With humans migrating out of Africa and inhabiting different parts of the earth, different populations have been under different selection pressures leading to adaptations in specific genes. Several methods have been developed to identify these sites of selection, but there is a varying level of uniformity among them. In this study, a unified approach for identifying sites of selection was applied to 17 different populations belonging to the Phase III of the 1000 Genomes Project. Combining different methods which capture different signs of selection, we identified several single nucleotide polymorphism (SNP) under selection using a machine learning model. We studied SNP and the populations showing high probability scores for selection, relating the SNPs with their significant eQTLs and phenotype to gain novel insights into the adaptations provided by them. A much in-depth analysis of these SNPs could not only help in understanding the history of human evolution but also generate hypotheses for better drug selection based on ethnicity.
