Abstract
The fine-scale genetic structure within populations, focusing on demographic histories and migration patterns, has been explored previously. However, limited attention has been paid to understanding how genetic structure influences lifestyle and dietary habits within an epidemiological framework. This study explores the fine-scale genetic structure within a homogeneous Japanese population using advanced unsupervised learning techniques-Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), and Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-coupled with direct-to-consumer genetic testing data. We investigate the associated genetic factors and examine the relationship between the genetic structure and geographic ancestry. Additionally, using cross-sectional data and multinomial logistic regression, we further elucidate the nuanced impacts of lifestyle and dietary factors across genetic clusters, emphasizing the importance of integrating genetic data with epidemiological research. This study introduces a new framework for genetic epidemiology that considers both genetic and environmental influences.