A new analysis introduces computational models that learn long-range regulatory interactions between genes, showing how distant genetic variants coordinate to influence schizophrenia, much like social networks connect distant people.

The study identified 641 previously unrecognized schizophrenia-associated genes by analyzing genetic data from over 102,000 individuals and postmortem brain tissue across six cortical regions.

These coordinated genetic programs could advance precision psychiatry, enabling treatments tailored to an individual’s biological gene-network profile.

Published in Nature Genetics and led by the Lieber Institute for Brain Development with international collaborators, the work underscores the role of gene networks in psychiatric disorders and translating genetics into actionable biology.

Findings point to core pathways such as glutamate signaling, intercellular communication, immune processes, and brain development as central to schizophrenia risk and potential treatment targets.

Schizophrenia risk is argued to arise from coordinated gene networks and systems-level genetic architecture rather than single sequentially acting genes.

Researchers from Johns Hopkins’ Lieber Institute and the University of Bari analyzed data from over 100,000 individuals and brain tissue across six regions from hundreds of donors.

Unlike traditional methods focused on variants near genes, this study shows most disease involvement comes from long-range gene interactions, expanding beyond proximal associations.

Moving beyond traditional local (cis) mapping, researchers use network-wide gene co-expression analyses to reveal that distant variants collectively influence schizophrenia risk.

The mapped genes cluster into pathways including glutamate signaling, synaptic communication, brain immune processes, and early neurodevelopment, highlighting broad pathway targets for therapy development.

The study advocates precision psychiatry, proposing interventions tailored to an individual’s integrated gene-network profile rather than single-gene targets.