Co-expression-based models improve eQTL predictions for transcriptome-wide association studies and highlight new schizophrenia-associated genes.

Authors

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

Published in

Nature genetics. Jun 22, 2026. Epub Jun 22, 2026.

Abstract

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 (P_FDR < 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.

PMID:
42332269
Bibliographic data and abstract were imported from PubMed on 23 Jun 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.