Variation in enhancer activity across brain regions defines neurological disease risk and shapes cellular pathology
Ramcharan, H. K.; Sami, Y. N.; Hazel, K.; Okeke, C.; Barnard, Z.; Hoang, A. T.; McCallum, S.; Apere, C.; Du, W.; Madden, M.; Huang, Y.; Luna, X.; Molla, G.; Mash, D.; Corradin, O.
Show abstract
Genetic variants associated with neurological traits are enriched in enhancers active in neurons. However, there are many types of neurons with varying functions across the brain, making it difficult to pinpoint insights into disease pathology. We set out to investigate whether cell type and brain region specificity of transcriptional enhancers could reveal new insights into the cellular pathology of neurological traits. We performed H3K27ac ChIP-seq on neurons and glia sorted from post-mortem tissue for six brain regions in triplicate and integrated these datasets with existing single-cell ATAC-seq data. While 87% of neuronal enhancers had similar activity across brain regions, we identified 40,049 neuronal regulatory elements that vary in activity across brain regions, which we termed Brain Region Variable Elements (BRVEs). Some BRVEs reflect differences in cell composition, such as a high proportion of medium spiny neurons (MSNs) in the nucleus accumbens, whereas others capture developmental trajectories, including enhancers with robust activity across telencephalic regions that are inactive in the diencephalic hypothalamus. Genetic variants within BRVEs are disproportionately enriched for the heritability of neurological traits, capturing far more heritability than neuronal enhancers with uniform activity across the brain. Additionally, genes linked to BRVEs are also more likely to be implicated in neuropsychiatric disorders by differential expression studies. Finally, we present a new method, GWAS-LOCATE, that leverages variation in enhancer activity across the brain to assign 23,494 GWAS loci to specific cell types and brain regions, including 9.8% linked to MSNs. This includes a BMI risk locus linked to the transcription factor ISL1. ISL1 knockdown in iPSC-derived MSNs revealed differentially expressed genes enriched for BMI heritability, supporting a role for ISL1-mediated MSN pathways with BMI. Collectively, these findings demonstrate that the enhancer specificity across the brain provides a powerful framework for dissecting complex trait biology and revealing cellular pathology.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- SingleBrain: A Meta-Analysis of Single-Nucleus eQTLs Linking Genetic Risk to Brain Disorders 98%
- cis-Regulatory Chromatin Contacts in Neural Cells Reveal Contributions of Genetic Variants to Complex Neurological Disorders 97%
- Population-level variation of enhancer expression identifies novel disease mechanisms in the human brain 96%
Similar papers in this journal
- Transcriptome and chromatin accessibility landscapes across 25 distinct human brain regions expand the susceptibility gene set for neuropsychiatric disorders 96%
- Single-nucleus epigenomic dysregulation unmasks genetic risk-associated neurodegenerative glia states 95%
- Single-cell spatial map of cis-regulatory elements for disease-related genes in the macaque cortex 95%
Similar papers in this journal
- Cell-type specific cis-eQTLs in eight brain cell-types identifies novel risk genes for human brain disorders 96%
- Acetylated Chromatin Domains Link Chromosomal Organization to Cell- and Circuit-level Dysfunction in Schizophrenia and Bipolar Disorder 95%
- Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex 95%
Similar papers in this journal
Similar papers in this journal
- Longitudinal single-cell transcriptional dynamics throughout neurodegeneration in SCA1 94%
- A systems biology-based identification and in vivo functional screening of Alzheimer's disease risk genes reveals modulators of memory function 94%
- Pathogenic DDX3X mutations impair RNA metabolism and neurogenesis during fetal cortical development 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.