Abstract

Depression is a complex and multifactorial disorder influenced by both genetic predisposition and gut microbiome 
composition. Polygenic risk scores (PRSs) capture the aggregate effect of thousands of genetic variants associated 
with depression, while microbiome profiles provide insight into gut microbial taxa and their neuroactive 
metabolite contributions. Integrating these modalities offers a promising approach for personalized mental health 
risk prediction. This review outlines conceptual foundations, methodological frameworks, and practical 
considerations for combining PRS and microbiome data, emphasizing interpretability, bias, equity, and real-world 
deployment. We discuss preprocessing, feature engineering, multimodal modeling approaches, and evaluation 
metrics, highlighting challenges such as population stratification, sampling bias, and cross-platform robustness. 
Ethical, legal, and social implications, including stigmatization, discrimination, and regulatory compliance, are 
critically examined. Case studies and simulation results illustrate enhanced predictive accuracy and clinical utility 
of integrated models. Finally, we provide recommendations for researchers and healthcare systems to advance 
responsible, equitable, and interpretable implementation of polygenic–microbiome integrative models for 
depression risk prediction.