Deep learning for psychiatric genomics: from tools to applications

Clinical Snapshot

Key Findings

• Effect Size: Not applicable - narrative review

• Primary Outcome: Evolution and applications of deep learning methods in psychiatric genomics

• Nnt Or Sensitivity: Not applicable - methodological review

Clinical Application

Implementation requires significant computational expertise and resources; limited immediate clinical applicability Relevant to Australian psychiatric genomics research initiatives; may inform future TGA considerations for genomic-based diagnostics; limited immediate impact on RACGP clinical guidelines Researchers in psychiatric genomics, bioinformaticians, and clinicians interested in precision psychiatry

Abstract

The genetic architecture of psychiatric disorders is highly complex, with genome-wide association studies implicating thousands of risk loci. A central challenge is that most of these variants are located in noncoding regions, making it difficult to elucidate their regulatory consequences within the brain's intricate cellular landscape. The recent convergence of advanced artificial intelligence, particularly deep learning (DL), has catalyzed a paradigm shift by providing powerful tools to address this gap. This review traces the evolution of DL in genomics, beginning with task-specific models. We then examine the transformative impact of foundation models, pretrained neural networks that learn the 'language' of biology, including genomic language models, single-cell foundation models, and large language models originally trained on natural language. Finally, we survey applications to key problems in psychiatric genomics. We hope this review provides a comprehensive overview of recent advances in DL for genomics and serves as a bridge to help researchers in psychiatric genomics more effectively understand and apply these frontier methods to guide the development of novel therapeutic strategies for psychiatric disorders.