Summary: Researchers have revolutionized psychiatric genomics by identifying 641 previously unrecognized genes associated with schizophrenia. The study analyzed genetic datasets from over 102,000 individuals alongside postmortem brain tissue samples spanning six distinct cortical regions. Moving past traditional “local-only” mapping techniques, which limit searches to DNA variants in immediate proximity to a gene, the team engineered advanced computational models that map long-range, network-wide regulatory relationships.
This approach, which mirrors human social networks connecting distant individuals, revealed that long-range genetic variants coordinate extensively across the brain to drive schizophrenia risk, fundamentally shifting the search toward complex biological pathways like glutamate signaling and brain-cell development.
Key Facts
- 641 Novel Genes Discovered: By mapping the distributed communication networks of genes rather than looking at individual units in isolation, researchers brought hundreds of overlooked risk genes to light.
- Overcoming the “Lamppost Effect”: Traditional studies have historically focused strictly on adjacent DNA mutations. This study proved that the vast majority of genetic involvement in schizophrenia relies on long-distance, network-wide co-expression.
- Massive Global Dataset: The computational breakthrough was fueled by an immense sample size, combining genetic profiles from 102,000+ individuals with multi-region brain tissue biopsies from hundreds of donors.
- Core Biological Pathways: The newly mapped gene clusters primarily regulate glutamate neurotransmitter signaling, synaptic communication, immune responses, and early neurodevelopment.
- Foundation for Precision Psychiatry: Shifting focus from fragmented, single-gene tracking to integrated genetic programs allows scientists to design localized therapeutic interventions customized to a patient’s exact network profile.
Source: Lieberman Institute
Scientists have long known that schizophrenia runs in families, but pinpointing exactly which genes contribute to risk has been like searching for needles in a haystack.
Now, researchers at the Lieber Institute for Brain Development and a consortium of collaborators from the University of Bari, Italy, and over 60 psychiatric hospitals all over the world, have developed a groundbreaking approach that looks beyond individual genes. They have uncovered how networks of genes communicate across the brain — revealing 641 previously unrecognized genes associated with schizophrenia.
The study, published in Nature Genetics, analyzed genetic data from over 102,000 individuals and brain tissue samples from hundreds of donors across six different brain regions. Traditional methods to associate genes with diseases only examine variants in the immediate proximity of the genes considered. However, the field is aware that the lion’s share of genes’ involvement in a disease depends on long-distance variants.
The research team developed new computational models that capture long-range regulatory relationships among genes — similar to how social networks connect people who don’t live next door to each other. This enhanced modeling framework enabled the discovery of hundreds of schizophrenia-associated genes that would not have been detected otherwise.
“Most genetic studies have been looking for the light under the lamppost, focusing only on genes close to disease-associated DNA variants,” said Dr. Giulio Pergola, senior author and researcher at the Lieber Institute for Brain Development.
“By incorporating gene co-expression networks, we’ve essentially turned on lights across the entire neighborhood, revealing how distant genetic variants coordinate to build the genetic basis of schizophrenia.”
The findings point to biological pathways involved in glutamate signaling, brain-cell communication, immune processes, and brain development — pathways that may help guide future research into new treatment strategies.
“This work demonstrates that schizophrenia risk isn’t just about individual genes acting one after another — it’s about how networks of genes work together,” said Dr. Daniel Weinberger, CEO and Director of the Lieber Institute for Brain Development. “Understanding these coordinated genetic programs brings us closer to precision psychiatry, where treatments can be tailored to an individual’s specific biological profile.”
The research represents a significant advance in translating genetic discoveries into actionable biological insights for one of psychiatry’s most burdensome disorders.
Key Questions Answered:
A: Traditional genomic research has suffered from what scientists call the “light under the lamppost” effect. Historically, mapping tools only looked for genetic variants that sat directly next to the genes they regulated. However, real human biology is highly interconnected. By looking only at immediate, local neighborhoods, previous studies completely missed how variations on one side of the genome can act as a long-distance switch for a gene located far away.
A: The consortium built advanced computational models designed to analyze gene co-expression patterns. By combining the genetic data of over 102,000 individuals with physical tissue samples taken from six distinct regions of human donor brains, they tracked which distant genes consistently fired up or silenced together. This allowed them to construct a complex network map, showing that even if two genetic elements are physically far apart, they are functionally part of the same cellular conversation.
A: Rather than pointing to a single root cause, the newly identified genes cluster into specific, essential biological pathways. They are heavily involved in controlling glutamate signaling (how neurons pass fast chemical messages), regulating synaptic communication, managing local brain immune processes, and guiding early embryonic brain development. Identifying these coordinated programs gives researchers concrete, pathway-wide targets for next-generation drug development.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- Journal paper reviewed in full.
- Additional context added by our staff.
About this genetics and schizophrenia research news
Author: Gideon Hertz
Source: Lieber Institute for Brain Development
Contact: Gideon Hertz – Lieber Institute for Brain Development
Image: The image is credited to Neuroscience News
Original Research: Open access.
“Co-expression-based models improve eQTL predictions for transcriptome-wide association studies and highlight new schizophrenia-associated genes” by Fabiana Rossi, Leonardo Sportelli, Gianluca C. Kikidis, Giulia Grassi, Fabio Di Camillo, Alessandro Bertolino, Giuseppe Blasi, Christopher J. Borcuk, Daniela Fusco, Thomas M. Hyde, Joel E. Kleinman, Davide Marnetto, Silvia Pellegrini, Antonio Rampino, Benedetto Vitiello, Stephan Ripke, Alice Braun, Julia Kraft, Sintia Iole Belangero, Paulo R. Menezes, Celso Arango, James T. R. Walters, Michael C. O’Donovan, Michael J. Owen, David Braff, Aiden Corvin, Derek W. Morris, Enrico Domenici, Jim van Os, Esref Atbaşoğlu, Meram C. Saka, Marta Di Forti, Bernhard T. Baune, Carlos N. Pato, Andrew McQuillin, Vera Golimbet, Nikolay Kondratyev, Valentina Escott-Price, Anna Gareeva, Elza Khusnutdinova, Jorge A. Cervilla, Margarita Rivera, Dominique Campion, Claudine Laurent-Levinson, Alessandro Serretti, Ole A. Andreassen, David St. Clair, Todd Lencz, Anil K. Malhotra, Nina S. McCarthy, Bryan J. Mowry, Dan Rujescu, Ina Giegling, Annette M. Hartmann, Bettina Konte, Markus M. Nöthen, Marcella Rietschel, George Kirov, Patrick F. Sullivan, Tracey L. Petryshen, Thomas Werge, Andrew M. McIntosh, Tõnu Esko, Erik G. Jönsson, Hannelore Ehrenreich, Brien P. Riley, Douglas F. Levinson, Joseph D. Buxbaum, Elvira Bramon, Christina M. Hultman, Roel A. Ophoff, Rolf Adolfsson, Eli A. Stahl, Sinan Guloksuz, Bart P. F. Rutten, Cristina M. Del-Ben, Florence Thibaut, Daniel R. Weinberger & Giulio Pergola. Nature Genetics
DOI:10.1038/s41588-026-02646-3
Abstract
Co-expression-based models improve eQTL predictions for transcriptome-wide association studies and highlight new schizophrenia-associated genes
Most genetic variants associated with complex heritability phenotypes lie in non-coding regions and are thought to influence disease risk by regulating gene expression. However, most transcriptome-wide association approaches primarily model local (cis) genetic effects, leaving much of gene regulation unexplained.
Here, we show that incorporating distal (trans) regulatory effects improves the prediction of gene expression and the identification of disease-associated genes.
Using RNA sequencing data from six human post-mortem brain regions, we developed INGENE and MODULE, two models capturing the combined influence of candidate trans-acting variants within gene coexpression networks.
Integrating these models with conventional cis-based predictors improved gene expression imputation (maximum likelihood estimation, α = 0.05) for 18,744 genes across regions.
Applying this framework to Psychiatric Genomics Consortium wave 3 genotypes identified 766 genes associated with schizophrenia (PFDR < 0.01), including 641 not previously reported by transcriptome-wide analyses.
These findings highlight the contribution of distal regulatory mechanisms and gene network interactions to schizophrenia risk.
