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Brain disease significantly impacts gene-phenotype links, altering genetic inferences. Analyzing non-diseased brains clarifies gene effects and identifies potential biomarkers for central nervous system (CNS) conditions.

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Area of Science:

  • Neurogenetics
  • Systems Biology
  • Genomics

Background:

  • Gene expression quantitative trait loci (eQTLs) are crucial for linking genes to central nervous system (CNS) phenotypes.
  • The influence of brain disease on these gene-trait associations remains largely unknown.
  • Understanding these effects is vital for accurate genetic interpretation in neurological disorders.

Purpose of the Study:

  • To investigate how brain disease affects gene expression and its correlation with genetic variation.
  • To determine the reliability of eQTLs in disease versus non-diseased brain tissue.
  • To identify robust gene-trait associations and potential biomarkers for CNS outcomes.

Main Methods:

  • Analysis of 2,348,438 single-nuclei profiles from 391 human brains (both disease cases and controls).
  • Identification of genes with expression correlated to genetic variation, assessing disease-dependent allelic effects.
  • Colocalization analysis of genetic variants with 30 CNS traits.
  • Application of single-cell Mendelian randomization in non-diseased brains.
  • Replication of findings in the UK Biobank.

Main Results:

  • 13,939 genes showed expression correlated with genetic variation; 16.7-40.8% exhibited disease-dependent allelic effects.
  • 23.6% of 501 gene-trait colocalizations showed disease dependency.
  • Analysis of non-diseased brains (n=183) revealed 91 additional colocalizations missed in the mixed cohort.
  • Single-cell Mendelian randomization in control brains identified 140 putatively causal gene-trait associations.
  • 11 of these associations were replicated in the UK Biobank.

Conclusions:

  • Brain disease significantly confounds genetic inferences of gene-phenotype relationships.
  • Using non-diseased brain data enhances the accuracy of eQTL interpretation and variant association.
  • This approach successfully prioritizes candidate peripheral biomarkers for CNS outcomes.