Causal Genomics in the Deep Learning Era

Understanding the molecular causes of complex diseases remains one of the most pressing challenges in biomedicine. Despite large-scale genome-wide association studies (GWAS) mapping thousands of risk loci, identifying which genetic variants truly drive disease remains difficult. Traditional statistical genetics has laid a strong foundation for variant discovery, but it often struggles to capture non-linear interactions and cannot fully integrate the breadth of the interconnected multi-omics data. In recent years, deep learning approaches have shown promise in bridging these gaps: modeling high-order genetic interactions, uncovering latent biological structure, and enabling multi-layered data integration. However, issues like overfitting, lack of interpretability, and limited statistical rigor have slowed their adoption in causal inference and therapeutic target discovery. In this review, we explore how traditional statistical and deep learning methods can be applied to uncover causal mechanisms in complex disease. We critically examine the strengths and blind spots of traditional GWAS, post-GWAS fine-mapping, and Mendelian randomization, and contrast them with emerging deep learning frameworks for epistasis detection and multi-omics integration. Finally, we propose a future direction centered around hybrid models that blend the scalability of deep learning with the inferential power of statistical genetics. Our goal is to guide researchers in developing next-generation computational tools to uncover the molecular basis of complex diseases and accelerate the translation of genetic findings into effective treatments.