This study, based on the UK Biobank database, integrates Polygenic Risk Score (PRS), Phenome-Wide Association Study (PheWAS), and Mendelian Randomization (MR) methods to explore how genetic and behavioral factors (such as sleep quality, dietary habits, social support, etc.) jointly influence the onset of depressive symptoms and their causal mechanisms. Depression, as a complex mental disorder, has a multifactorial etiology. Although a large number of studies have focused on its genetic basis and environmental factors, the interaction between the two remains unclear. This study aims to fill this gap by focusing on the interaction mechanisms between genetic susceptibility and behavioral phenotypes.
Specifically, this study will: (1) use the PRS method to assess an individual’s genetic susceptibility and investigate how it interacts with behavioral factors to influence depression risk; (2) use the PheWAS method to systematically identify behavioral phenotypes associated with depression, and identify potential risk and protective factors; (3) apply the MR method to verify the causal relationships between genetic and behavioral factors, addressing confounding bias in traditional observational studies; (4) construct a multidimensional depression prediction model based on genetic and behavioral data, and improve the model’s accuracy and clinical application value using machine learning methods.
The scientific significance of this study lies in providing theoretical support and data evidence for the early identification, precise intervention, and personalized treatment of depression through accurate multidimensional data analysis. In addition, the results are expected to provide scientific evidence for the formulation of public health policies, promoting the improvement of prevention and treatment strategies for depression in society.
