From molecules to minds: Integrative multi-omics in psychiatry.

Authors

Hossein Abbasi, Sage E Hawn, Arash Javanbakht, Soraya Seedat, Kyle Bourassa, Sinead M Sinnott, Antonia V Seligowski, Sian Hemmings, Nathan A Kimbrel, Erika Wolf, Alicia K Smith, Leslie Brick, Divya Mehta

Published in

Journal of mood and anxiety disorders. Volume 15. Pages 100194. Epub Jul 29, 2026.

Abstract

Psychiatric disorders are biologically complex conditions arising from interactions across genomic, epigenomic, transcriptomic, proteomic, metabolomic, and metagenomic layers. Single-omics approaches rarely capture more than a fraction of the variance in complex conditions, underscoring the importance of integrative multi-omics frameworks. This mini-review summarizes key methodologies and their application in psychiatric research, with a focus on systems-level integration of genomic risk scores, transcriptomic networks, and neuroimaging data to advance biological understanding of disorders such as depression, schizophrenia, and Alzheimer's disease. We also outline the infrastructural requirements for effective multi-omics research, including standardized biobanking, Laboratory Information Management Systems, adherence to FAIR data principles, and federated learning approaches for privacy-preserving analysis. Importantly, we highlight the need for greater global inclusivity in psychiatric genomics. Current datasets are heavily biased toward relatively high-resourced and predominantly White, non-Hispanic populations, limiting generalizability. Initiatives such as the Psychiatric Genomics Consortium-Africa and H3ABioNet demonstrate how locally led efforts can strengthen capacity, promote data sovereignty, and support equitable research practices. Advancing multi-omics psychiatry will require coordinated investment in infrastructure, training, and inclusive international collaboration. This mini-review serves primarily as a conceptual roadmap, highlighting what integrative approaches have demonstrated so far and future directions for the field.

PMID:
42598558
Bibliographic data and abstract were imported from PubMed on 14 Aug 2026.

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