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Cell-type-specific cis-eQTLs in eight human brain cell types identify novel risk genes for psychiatric and neurological disorders

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TLDR
This article performed an eQTL analysis using single-nuclei RNA sequencing from 192 individuals in eight brain cell types derived from the prefrontal cortex, temporal cortex and white matter, and identified 7,607 eGenes, a substantial fraction (46%, 3,537/7,607) of which show cell-type-specific effects with strongest effects in microglia.
Abstract
To date, most expression quantitative trait loci (eQTL) studies, which investigate how genetic variants contribute to gene expression, have been performed in heterogeneous brain tissues rather than specific cell types. In this study, we performed an eQTL analysis using single-nuclei RNA sequencing from 192 individuals in eight brain cell types derived from the prefrontal cortex, temporal cortex and white matter. We identified 7,607 eGenes, a substantial fraction (46%, 3,537/7,607) of which show cell-type-specific effects, with strongest effects in microglia. Cell-type-level eQTLs affected more constrained genes and had larger effect sizes than tissue-level eQTLs. Integration of brain cell type eQTLs with genome-wide association studies (GWAS) revealed novel relationships between expression and disease risk for neuropsychiatric and neurodegenerative diseases. For most GWAS loci, a single gene co-localized in a single cell type, providing new clues into disease etiology. Our findings demonstrate substantial contrast in genetic regulation of gene expression among brain cell types and reveal potential mechanisms by which disease risk genes influence brain disorders. Bryois et al. mapped genetic variants regulating gene expression in eight major brain cell types. They found a large number of cell-type-specific genetic effects and leveraged their results to identify novel putative risk genes for brain disorders.

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Citations
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The missing link between genetic association and regulatory function

- 14 Dec 2022 - 
TL;DR: In this article , the authors identify 220 gene-trait pairs in which protein-coding variants influence a complex trait or its Mendelian cognate, and find limited evidence that the baseline expression of trait-related genes explains GWAS associations, whether using colocalization methods (8% of genes implicated), transcription-wide association (2% of gene implicated), or a combination of regulatory annotations and distance (4%of genes implicated).
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Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases

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Genome-wide association study of REM sleep behavior disorder identifies polygenic risk and brain expression effects

Lynne Krohn, +105 more
TL;DR: This paper performed a genome-wide association study of RBD, identifying five RBD risk loci near SNCA, GBA, TMEM175, INPP5F, and SCARB2.
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Single nucleus multiomics identifies ZEB1 and MAFB as candidate regulators of Alzheimer’s disease-specific cis-regulatory elements

TL;DR: This paper performed single nucleus multiomics (snRNA-seq plus snATAC-seq) on 105,332 nuclei isolated from cortical tissues from 7 AD and 8 unaffected donors to identify candidate cis-regulatory elements (CREs) involved in AD-associated transcriptional changes.
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PICALM and Alzheimer’s Disease: An Update and Perspectives

TL;DR: The PICALM (Phosphatidylinositol binding clathrin-assembly protein) gene has been identified as the most significant genetic susceptibility locus after APOE and BIN1 as discussed by the authors .
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