TY - JOUR
KW - Computational models
KW - Data integration
KW - Gene expression
KW - Quantitative Trait Loci
AU - Sanan Venkatesh
AU - Roman Kosoy
AU - Zhenyi Wu
AU - Marios Anyfantakis
AU - Christian Dillard
AU - Prashant N. M.
AU - David Burstein
AU - Deepika Mathur
AU - Chris Chatzinakos
AU - Bukola Ajanaku
AU - Fotis Tsetsos
AU - Biao Zeng
AU - Sonali Gupta
AU - Rachel Bercovitch
AU - Aram Hong
AU - Clara Casey
AU - Marcela Alvia
AU - Zhiping Shao
AU - Stathis Argyriou
AU - Karen Therrien
AU - Tim Bigde li
AU - Pavan Auluck
AU - David A. Bennett
AU - Stefano Marenco
AU - Vahram Haroutunian
AU - Kiran Girdhar
AU - Jaroslav Bendl
AU - Donghoon Lee
AU - John F. Fullard
AU - Gabriel E. Hoffman
AU - Georgios Voloudakis
AU - Panos Roussos
AB - Common brain disorders impose a substantial health burden, but localizing their genetic risk in the brain remains challenging1. Although genome-wide association studies have identified numerous loci associated with neuropsychiatric and neurodegenerative disorders, many of these loci lie in non-coding regions that influence gene expression in specific cell types2–5. Traditional bulk brain transcriptomic analyses, which often focus on European ancestry cohorts, average over cellular diversity, obscuring genetic risk-related changes in gene expression. Here we use single-nucleus gene expression profiles from the dorsolateral prefrontal cortex in the multi-ancestry PsychAD cohort to develop transcriptomic imputation models of genetically regulated expression across major brain cell types. Applying these models to neuropsychiatric and neurodegenerative disorders reveals thousands of gene–trait associations that are undetectable in bulk tissue analyses and resolves many signals to discrete neuronal, glial and immune cell populations. Cross-ancestry analyses in the Million Veteran Program confirm these associations, reveal pleiotropic effects of cell-type-specific predicted expression and demonstrate that trait-related dysregulation is conserved across ancestries, enabling mapping of causal genes and pathways. Together, these findings provide a cell-type-resolved and ancestry-aware atlas of genetically regulated expression in the human prefrontal cortex and illustrate how single-nucleus transcriptomics can sharpen gene discovery and therapeutic target prioritization for complex brain disorders.
BT - Nature
DA - 2026-09
DO - 10.1038/s41586-026-10836-6
IS - 8133
LA - en
N2 - Common brain disorders impose a substantial health burden, but localizing their genetic risk in the brain remains challenging1. Although genome-wide association studies have identified numerous loci associated with neuropsychiatric and neurodegenerative disorders, many of these loci lie in non-coding regions that influence gene expression in specific cell types2–5. Traditional bulk brain transcriptomic analyses, which often focus on European ancestry cohorts, average over cellular diversity, obscuring genetic risk-related changes in gene expression. Here we use single-nucleus gene expression profiles from the dorsolateral prefrontal cortex in the multi-ancestry PsychAD cohort to develop transcriptomic imputation models of genetically regulated expression across major brain cell types. Applying these models to neuropsychiatric and neurodegenerative disorders reveals thousands of gene–trait associations that are undetectable in bulk tissue analyses and resolves many signals to discrete neuronal, glial and immune cell populations. Cross-ancestry analyses in the Million Veteran Program confirm these associations, reveal pleiotropic effects of cell-type-specific predicted expression and demonstrate that trait-related dysregulation is conserved across ancestries, enabling mapping of causal genes and pathways. Together, these findings provide a cell-type-resolved and ancestry-aware atlas of genetically regulated expression in the human prefrontal cortex and illustrate how single-nucleus transcriptomics can sharpen gene discovery and therapeutic target prioritization for complex brain disorders.
PY - 2026
SP - 1016
EP - 1026
T2 - Nature
TI - Single-nucleus transcriptome-wide association study of human brain disorders
UR - https://www.nature.com/articles/s41586-026-10836-6
VL - 657
Y2 - 2026-09-25
SN - 1476-4687
ER -