A meta-analysis of harmonized human brain RNA-seq datasets creates expression quantitative trait locus (eQTL) maps for multiple ancestries and brain regions, predicts cell-type-dependent eQTLs and produces gene networks. This prioritizes genes for multiple brain-related diseases, serving as a promising step toward the identification of central nervous system (CNS) drug targets.
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References
The GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020). Describes a project aimed at unravelling human genetic traits and diseases by characterizing expression variation across individuals and tissues using eQTLs.
Bryois, J. et al. Cell-type-specific cis-eQTLs in eight human brain cell types identify novel risk genes for psychiatric and neurological disorders. Nat. Neurosci. 25, 1104–1112 (2022). Presents cell-type-specific eQTL effects derived from single-cell data in human brain.
Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018). A human brain resource aimed at unravelling human psychiatric disorders, which presents, among other things, cell-type proportion estimates in bulk tissue samples.
van der Wijst, M. et al. The single-cell eQTLGen consortium. eLife 9, e52155 (2020). Presents a consortium (sc-eQTLGen) that aims to pinpoint the cellular contexts in which disease-causing genetic variants affect gene expression.
Võsa, U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 53, 1300–1310 (2021). Presents a consortium (eQTLGen) that aims to identify the downstream consequences of trait-related genetic variants using eQTLs in the blood.
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This is a summary of: de Klein, N. et al. Brain expression quantitative trait locus and network analysis reveal downstream effects and putative drivers for brain-related diseases. Nat. Genet. https://doi.org/10.1038/s41588-023-01300-6 (2023).
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Meta-analysis of harmonized brain transcriptomics data prioritizes therapeutic target genes. Nat Genet 55, 363–364 (2023). https://doi.org/10.1038/s41588-023-01301-5
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DOI: https://doi.org/10.1038/s41588-023-01301-5
