Cell state-dependent allelic effects and contextual Mendelian randomization analysis for human brain phenotypes
Year: 2025
Type: article
Study design: Observational Study
Abstract: Gene expression quantitative trait loci are widely used to infer relationships between genes and central nervous system (CNS) phenotypes; however, the effect of brain disease on these inferences is unclear. Using 2,348,438 single-nuclei profiles from 391 disease-case and control brains, we report 13,939 genes whose expression correlated with genetic variation, of which 16.7-40.8% (depending on cell type) showed disease-dependent allelic effects. Across 501 colocalizations for 30 CNS traits, 23.6% had a disease dependency, even after adjusting for disease status. To estimate the unconfounded effect of genes on outcomes, we repeated the analysis using nondiseased brains (n = 183) and reported an additional 91 colocalizations not present in the larger mixed disease and control dataset, demonstrating enhanced interpretation of disease-associated variants. Principled implementation of single-cell Mendelian randomization in control-only brains identified 140 putatively causal gene-trait associations, of which 11 were replicated in the UK Biobank, prioritizing candidate peripheral biomarkers predictive of CNS outcomes.
Source: Nature Genetics
Authors: Alexander Haglund, Verena Zuber, Maya Abouzeid, Yifei Yang, Jeong Hun Ko, Liv Wiemann, Maria Otero-Jimenez, Louwai Muhammed, Rahel Feleke, Alexi Nott, James D. Mills, Liisi Laaniste, Djordje O. Gveric, Daniel Clode, Ann C. Babtie, Susanna Pagni, Ravishankara Bellampalli, Alyma Somani, Karina McDade, Jasper J. Anink, Lucia Mesarosova, Nurun Fancy, Nanet Willumsen, Amy Smith, Johanna Jackson, Javier Alegre-Abarrategui, Eleonora Aronica, Paul M. Matthews, Maria Thom, Sanjay M. Sisodiya, Prashant K. Srivastava, Dheeraj Malhotra, Julien Bryois, Leonardo Bottolo, Michael R. Johnson
Institutions: Imperial College London, UK Dementia Research Institute, MRC Centre for Environment and Health, University of Oxford, Amsterdam Neuroscience, Amsterdam University Medical Centers, UCL Queen Square Institute of Neurology, University College London, Epilepsy Society, University of Amsterdam, University of Exeter, University of Edinburgh, Stichting Epilepsie Instellingen Nederland, Roche (Switzerland), Biogen (United States), University of Cambridge, The Alan Turing Institute, MRC Biostatistics Unit
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $12,690
Funders: Alan Turing Institute, Katy Baggott Foundation, UK Research and Innovation, Alzheimer's Society, Alzheimer Society, National Institute for Health and Care Research, Department of Health and Social Care, Parkinson's UK, Research Councils UK, Imperial College London, UK Dementia Research Institute, Epilepsy Society, Medical Research Council, Engineering and Physical Sciences Research Council, NIHR Oxford Biomedical Research Centre
Awards: G1309768829, G1430203965, G1502031688, G1633672591, G2233448381, G2537968379, G3855435662, G4321443121, G444630160, The Alan Turing Institute, G4753630297, G5300737479, G5420266884, An integrated systems-level framework for deciphering multidrug resistant epilepsy., G5753670155, G6094008768, G6292285981, G6564084049, Bayesian causal learning: A novel framework for drug-target discovery using Mendelian randomization on single-cell transcriptomics, G8581904048, G8795210754
OpenAlex ID: W4406232070