Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning

Bracher-Smith, Matthew and Escott-Price, Valentina ORCID: https://orcid.org/0000-0003-1784-5483 2025. Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning. Molecular Psychiatry 30 , pp. 5914-5925. 10.1038/s41380-025-03330-4

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Abstract

Machine learning (ML) is revolutionising our ability to decode the complex genetic architectures of brain disorders. In this review we examine the strengths and limitations of ML methods, highlighting their applications in genetic prediction, patient stratification, and the modelling of genetic interactions. We explore how ML can augment polygenic risk scores (PRS) through advanced techniques and how integrating functional genomics and multimodal data can address challenges like rare variants and weak genetic effects. Additionally, we discuss the importance of embedding biological knowledge into ML models to enhance interpretability and uncover meaningful insights. With the ongoing expansion of phenotype-genotype datasets and advances in federated learning, ML is poised to compete with and surpass classical statistical methods in disease risk prediction and identifying genetically homogenous subgroups. By balancing the strengths and weaknesses of these approaches, we provide a roadmap for leveraging ML to unravel the genomic complexity of brain disorders and drive the next wave of discoveries.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Medicine
Publisher:	Springer Nature [academic journals on nature.com]
ISSN:	1359-4184
Date of First Compliant Deposit:	11 November 2025
Date of Acceptance:	23 October 2025
Last Modified:	11 Nov 2025 14:15
URI:	https://orca.cardiff.ac.uk/id/eprint/182335

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