Anatomically Modern Humans (AMH) originated and adapted in Africa for a long time before moving Out of Africa(OoA) around 50000-100000 years before present and underwent several demographic transitions in the journey of peopling the Globe. During this journey, AMH faced a different environment than the African subcontinent environment. Over time, AMH subpopulations moved into different parts of the globe and adapted to the specific local environment. This journey draws attention to the constant interaction of AMH populations with the environment i.e. Genotype-Environment(GxE) interaction. The constant GxE interactions resulted in spatially varying phenotypic traits across populations living in a local geographical space. This research work aims to investigate the role of GxE interaction in the local adaptation of human populations. The study aims to develop Machine Learning and Deep Learning model for detecting the genetic variants associated with environmental factors and selected for adaptive phenotypic traits. Identifying genetic and environmental factors responsible for the phenotypic trait variation across populations is crucial in public health and this study sheds light on some of the factors responsible for spatially varying phenotypic traits across human populations residing in a local geographical space.
