Cell state-dependent allelic effects and contextual Mendelian randomization analysis for human brain phenotypes

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.

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Publication Details

Journal
Nature Genetics
Published
2025-01-10
DOI
https://doi.org/10.1038/s41588-024-02050-9
Citations
20
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
16.12

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Cell state-dependent allelic effects and contextual Mendelian randomization analysis for human brain phenotypes

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Cell state-dependent allelic effects and contextual Mendelian randomization analysis for human brain phenotypes

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article en
20 citations

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.

Nature GeneticsVol. 57(2)
Roche (Switzerland) (CH), Turing Institute (GB), Biogen (United States) (US), University of Exeter (GB), University of Cambridge (GB), University of Oxford (GB), Cambridge School (PT), Amsterdam Neuroscience (NL), UK Dementia Research Institute (GB), The Alan Turing Institute (GB), MRC Biostatistics Unit (GB), National Hospital for Neurology and Neurosurgery (GB), Amsterdam University Medical Centers (NL), Stichting Epilepsie Instellingen Nederland (NL), MRC Centre for Environment and Health, University College London (GB), Epilepsy Society (GB), Imperial College London (GB), University of Amsterdam (NL), University of Edinburgh (GB)
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, Imperial College London, UK Dementia Research Institute, Epilepsy Society, Medical Research Council, Engineering and Physical Sciences Research Council
Good health and well-being
Openalex Percentile: Top 1%
Single-cell and spatial transcriptomics
16.12
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