Chen Yichi, Katayama Kotoe, Ishida Sachiko, Imoto Seiya
Division of Health Medical Intelligence, Human Genome Center, the Institute of Medical Science, the University of Tokyo, Minato-ku, Tokyo, Japan.
Laboratory of Sequence Analysis, Human Genome Center, the Institute of Medical Science, the University of Tokyo, Minato-ku, Tokyo, Japan.
Commun Biol. 2025 Jul 12;8(1):1046. doi: 10.1038/s42003-025-08479-w.
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.
此前已经对群体内部的精细尺度遗传结构进行了探索，重点关注人口统计学历史和迁移模式。然而，在流行病学框架内，对于理解遗传结构如何影响生活方式和饮食习惯的关注却很有限。本研究使用先进的无监督学习技术——主成分分析（PCA）、均匀流形逼近与投影（UMAP）以及基于密度的带噪声应用空间聚类（DBSCAN），结合直接面向消费者的基因检测数据，探索了同质化日本人群体内部的精细尺度遗传结构。我们调查了相关的遗传因素，并研究了遗传结构与地理血统之间的关系。此外，利用横断面数据和多项逻辑回归，我们进一步阐明了生活方式和饮食因素对各遗传簇的细微影响，强调了将遗传数据与流行病学研究相结合的重要性。本研究引入了一个新的遗传流行病学框架，该框架同时考虑了遗传和环境影响。
