Медицинская библиотека · Nature genetics · 2026

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

Rossi Fabiana, Sportelli Leonardo, Kikidis Gianluca C, Grassi Giulia, Di Camillo Fabio, Bertolino Alessandro, Blasi Giuseppe, Borcuk Christopher J, Fusco Daniela, Hyde Thomas M, Kleinman Joel E, Marnetto Davide, Pellegrini Silvia, Rampino Antonio, Vitiello Benedetto, Ripke Stephan, Braun Alice, Kraft Julia, Belangero Sintia Iole, Menezes Paulo R, Arango Celso, Walters James T R, O'Donovan Michael C, Owen Michael J, Braff David, Corvin Aiden, Morris Derek W, Domenici Enrico, van Os Jim, Atbaşoğlu Esref, Saka Meram C, Di Forti Marta, Baune Bernhard T, Pato Carlos N, McQuillin Andrew, Golimbet Vera, Kondratyev Nikolay, Escott-Price Valentina, Gareeva Anna, Khusnutdinova Elza, Cervilla Jorge A, Rivera Margarita, Campion Dominique, Laurent-Levinson Claudine, Serretti Alessandro, Andreassen Ole A, St Clair David, Lencz Todd, Malhotra Anil K, McCarthy Nina S, Mowry Bryan J, Rujescu Dan, Giegling Ina, Hartmann Annette M, Konte Bettina, Nöthen Markus M, Rietschel Marcella, Kirov George, Sullivan Patrick F, Petryshen Tracey L, Werge Thomas, McIntosh Andrew M, Esko Tõnu, Jönsson Erik G, Ehrenreich Hannelore, Riley Brien P, Levinson Douglas F, Buxbaum Joseph D, Bramon Elvira, Hultman Christina M, Ophoff Roel A, Adolfsson Rolf, Stahl Eli A, Guloksuz Sinan, Rutten Bart P F, Del-Ben Cristina M, Thibaut Florence, Weinberger Daniel R, Pergola Giulio

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Аннотация

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.

Для цитирования: Rossi Fabiana, Sportelli Leonardo, Kikidis Gianluca C, Grassi Giulia, Di Camillo Fabio, Bertolino Alessandro, Blasi Giuseppe, Borcuk Christopher J, Fusco Daniela, Hyde Thomas M, Kleinman Joel E, Marnetto Davide, Pellegrini Silvia, Rampino Antonio, Vitiello Benedetto, Ripke Stephan, Braun Alice, Kraft Julia, Belangero Sintia Iole, Menezes Paulo R, Arango Celso, Walters James T R, O'Donovan Michael C, Owen Michael J, Braff David, Corvin Aiden, Morris Derek W, Domenici Enrico, van Os Jim, Atbaşoğlu Esref, Saka Meram C, Di Forti Marta, Baune Bernhard T, Pato Carlos N, McQuillin Andrew, Golimbet Vera, Kondratyev Nikolay, Escott-Price Valentina, Gareeva Anna, Khusnutdinova Elza, Cervilla Jorge A, Rivera Margarita, Campion Dominique, Laurent-Levinson Claudine, Serretti Alessandro, Andreassen Ole A, St Clair David, Lencz Todd, Malhotra Anil K, McCarthy Nina S, Mowry Bryan J, Rujescu Dan, Giegling Ina, Hartmann Annette M, Konte Bettina, Nöthen Markus M, Rietschel Marcella, Kirov George, Sullivan Patrick F, Petryshen Tracey L, Werge Thomas, McIntosh Andrew M, Esko Tõnu, Jönsson Erik G, Ehrenreich Hannelore, Riley Brien P, Levinson Douglas F, Buxbaum Joseph D, Bramon Elvira, Hultman Christina M, Ophoff Roel A, Adolfsson Rolf, Stahl Eli A, Guloksuz Sinan, Rutten Bart P F, Del-Ben Cristina M, Thibaut Florence, Weinberger Daniel R, Pergola Giulio. Co-expression-based models improve eQTL predictions for transcriptome-wide association studies and highlight new schizophrenia-associated genes.. Nature genetics. 2026. DOI: 10.1038/s41588-026-02646-3.

Предметные рубрики MeSH

Schizophrenia Humans Quantitative Trait Loci Genome-Wide Association Study Genetic Predisposition to Disease Gene Regulatory Networks Transcriptome Polymorphism, Single Nucleotide Models, Genetic Gene Expression Profiling Gene Expression Regulation Brain Genotype Phenotype

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