Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Meta-analysis of harmonized brain transcriptomics data prioritizes therapeutic target genes

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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Get just this article for as long as you need it


Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Meta-analysis of harmonized brain transcriptomics datasets.


  1. 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.

    Article  PubMed Central  Google Scholar 

  2. 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.

    Article  CAS  PubMed  Google Scholar 

  3. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  5. 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.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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. (2023).

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meta-analysis of harmonized brain transcriptomics data prioritizes therapeutic target genes. Nat Genet 55, 363–364 (2023).

Download citation

  • Published:

  • Issue Date:

  • DOI:


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing