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A meta-analysis of single-nucleus expression quantitative trait loci linking genetic risk to brain disorders

Nature Genetics (2026)Cite this article

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Abstract

Most genetic risk variants for neurological diseases are located in noncoding regulatory regions, where they often act as expression quantitative trait loci (eQTLs), modulating gene expression and influencing disease susceptibility. However, eQTL studies in bulk brain tissue or cell lines fail to capture the brain’s cellular diversity. Single-nucleus RNA sequencing (snRNA-seq) allows high-resolution mapping of eQTLs across diverse brain cell types. Here we performed a meta-analysis by integrating snRNA-seq and genotype data from four cohorts, totaling 5.8 million nuclei from 983 individuals of European ancestry. We mapped cis-eQTLs and trans-eQTLs across major brain cell types and subtypes, including disease-specific and sex-specific eQTLs, and applied colocalization and Mendelian randomization to identify genes that mediate neurological disease risk. We observed up to tenfold more cis-eQTLs and uncovered cell-type-specific genes linked to neurological disease. SingleBrain is a comprehensive single-cell eQTL resource that provides insights into the genetic mechanism of brain disorders.

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Fig. 1: Overview of SingleBrain.
Fig. 2: SingleBrain cis-expression and trans-eQTLs.
Fig. 3: Colocalization of SingleBrain eQTLs with neurodegenerative and neuropsychiatric disease GWAS loci.
Fig. 4: Microglia eQTLs are enriched in AD GWAS loci.
Fig. 5: Brain cell-type-specific eQTLs drive genetic risk in PD.
Fig. 6: Disease-associated eQTLs in microglia subtypes.

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Data availability

Genotypes from refs. 11,14 are available at https://doi.org/10.7303/syn10901595. snRNA-seq data from ref. 11 are available at https://www.synapse.org/Synapse:syn31512863. snRNA-seq data from ref. 14 are available at https://www.synapse.org/Synapse:syn52293417. snRNA-seq and genotype data from ref. 15 are available at https://www.synapse.org/Synapse:syn26223298. snRNA-seq data and genotype data for the Roche cohort, which is part of ref. 12, have been deposited in the European Genome-Phenome Archive, which is hosted by the European Bioinformatics Institute and the Center for Genomic Regulation, under accession EGAS00001006345. GTEx Consortium. The GTEx Project (v10) is available at https://gtexportal.org. The massive parallel reporter assay-SCZ dataset was downloaded from https://github.com/thewonlab/schizophrenia-MPRA. Epigenomic data from purified human microglia, neurons, astrocytes and oligodendrocytes19 were downloaded from https://github.com/nottalexi/brain-cell-type-peak-files. SingleBrain eQTL browser is accessible at https://singlebrain.nygenome.org. All cis-eQTL summary statistics are accessible through Zenodo at https://doi.org/10.5281/zenodo.14908182 (ref. 63). Bonferroni-corrected significant trans-eQTL summary statistics are accessible through Zenodo at https://doi.org/10.5281/zenodo.15860673 (ref. 64). All SAIGE-QTL summary statistics are accessible through Zenodo at https://doi.org/10.5281/zenodo.15860973 (ref. 65). All disease-specific and sex-specific eQTL summary statistics are accessible through Zenodo at https://doi.org/10.5281/zenodo.16051904 (ref. 66).

Code availability

QTL preparation and meta-analysis pipeline can be found on Zenodo at https://doi.org/10.5281/zenodo.18332620 (ref. 67). SAIGE-QTL pipeline is available through Zenodo at https://doi.org/10.5281/zenodo.18239115 (ref. 68). Downstream-QTL analysis pipeline is available through Zenodo at https://doi.org/10.5281/zenodo.18320789 (ref. 69). All codes used to produce analysis for figures are available through Zenodo at https://doi.org/10.5281/zenodo.18239090 (ref. 70).

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Acknowledgements

The authors thank the patients and their families who donated the materials for these studies. T.R. and J.B. received funding from Calico Life Sciences. T.R., B.J., W.H.D., T.N. and J.H. were supported by National Institutes of Health (NIH; grants NIA U01-AG068880, NIA R21-AG063130, NIA R01-AG054005, NIA RF1-AG065926, NIA R01-AG065926, NIA R56-AG088669, NIA R21-AG091272, NIA P30-AG066514, NINDS U54-NS123743 and NINDS R01-NS116006). H.-H.W. was supported by the National Research Foundation of Korea grant supported by the Korea government (MSIT; RS-2023-00223277 and RS-2023-00262527) and the Future Medicine 2030 Project of the Samsung Medical Center (SMX1250081). This work was supported in part by the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai and by the Clinical and Translational Science Awards grant (UL1TR004419) from the National Center for Advancing Translational Sciences. This research was supported by the Office of Research Infrastructure of the NIH (awards S10OD026880 and S10OD030463). The funders had no role in the design and conduct of the study; the collection, management, analysis and interpretation of the data; the preparation, review or approval of the manuscript; or the decision to submit the manuscript for publication.

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Authors and Affiliations

  1. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA

    Beomjin Jang, Kailash BP, Winston H. Dredge, Ashvin Ravi, Tatsuhiko Naito, Mikaela Rosen, Jack Humphrey & Towfique Raj

  2. Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, USA

    Beomjin Jang, Kailash BP, Winston H. Dredge, Ashvin Ravi, Tatsuhiko Naito, Mikaela Rosen, Joel Blanchard, Jack Humphrey & Towfique Raj

  3. Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York City, NY, USA

    Beomjin Jang, Kailash BP, Winston H. Dredge, Ashvin Ravi, Tatsuhiko Naito, Mikaela Rosen, Joel Blanchard, Jack Humphrey & Towfique Raj

  4. Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA

    Beomjin Jang, Kailash BP, Winston H. Dredge, Ashvin Ravi, Tatsuhiko Naito, Mikaela Rosen, Jack Humphrey & Towfique Raj

  5. Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA

    Beomjin Jang, Kailash BP, Winston H. Dredge, Ashvin Ravi, Tatsuhiko Naito, Mikaela Rosen, Jack Humphrey & Towfique Raj

  6. Department of Digital Health, SAIHST, Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea

    Beomjin Jang, Beomsu Kim, Minyoung Cho, Mi-So Park & Hong-Hee Won

  7. Departments of Computer Science and Systems Biology, Columbia University, New York City, NY, USA

    Alex Tokolyi & David A. Knowles

  8. New York Genome Center, New York City, NY, USA

    Alex Tokolyi & David A. Knowles

  9. Biological and Medical Informatics PhD Program, University of California, San Francisco, San Francisco, CA, USA

    Ashvin Ravi

  10. Department of Medical Informatics, Kangwon National University College of Medicine, Chuncheon, Republic of Korea

    Sang-Hyuk Jung

  11. Division of Nephrology, Boston Children’s Hospital, Boston, MA, USA

    Beomsu Kim

  12. Kidney Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Beomsu Kim

  13. Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Min Seo Kim

  14. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA

    Min Seo Kim

  15. Department of Precision Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Hong-Hee Won

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Contributions

T.R. conceived and designed the study. B.J. analyzed the data and performed statistical analyses with assistance from K.B.P., W.H.D., A.R., S.-H.J., T.N., B.K., M.S.K., M.C., M.-S.P., M.R. and J.H., supervised by H.-H.W. and T.R. A.T. constructed its website. B.J., J.B., H.-H.W. and T.R. interpreted the results. B.J. and T.R. wrote the manuscript. J.H., D.A.K. and H.-H.W. critically reviewed this study. All the authors have read and approved the final manuscript. T.R. and H.-H.W. supervised the study.

Corresponding authors

Correspondence to Hong-Hee Won or Towfique Raj.

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Competing interests

T.R. served as a scientific advisor for Merck and a consultant for Curie.Bio. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Julien Bryois, Sarah Gagliano Taliun, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 SAIGE-QTL and colocalization compared to SingleBrain.

a, Comparison of the number of eGenes (q-value < 0.05) identified in this study (SingleBrain, n = 983), SAIGE-QTL of ROS/MAP-Columbia (n = 397), and eQTL summary statistics11. b,c, Pairwise sharing of genes with significant cis-eQTLs across major cell types. The numbers shown are Storey’s π1 for each cell type pair. π1 is an estimate of the proportion of true alternative hypotheses in the replication cell type, derived from the distribution of P-values. The columns are used for brain major eQTL lead single-nucleotide polymorphisms (SNPs) identified in SAIGE-QTL, and the replication rates were obtained from (b) ROS/MAP-Columbia11 and (c) this study. d, Scatterplots of effect size estimates from SAIGE-QTL (x-axis) and this study (y-axis) are shown for each of the seven cell types. Top variants in eGenes identified in SAIGE-QTL and eQTL variants of eGenes in SingleBrain were included. The Rb statistic20 indicates the correlation of the effect size estimates between matched SNP–gene pairs. e, Number of AD and PD GWAS loci with a PP4 > 0.8. The dark bars represent SingleBrain, and the light bars represent SAIGE-QTL. f,g, The SCAF11 PD GWAS locus and PTK2B AD GWAS locus at astrocytes ANO6 eQTL and oligodendrocytes PTK2B eQTL in SAIGE-QTL and this study. Reported P values are two-sided and not corrected for multiple testing. The x-axis and the y-axis represent the −log10(P value) for the association of variants with AD and PD, and gene expression, respectively. Single-nucleotide polymorphisms (SNPs) are colored by linkage disequilibrium (LD) with the lead GWAS SNP. MG, microglia; Ext, excitatory neuron; IN, inhibitory neuron; OD, oligodendrocyte; Ast, astrocyte; OPC, oligodendrocyte progenitor cell; End, endothelial cell.

Extended Data Fig. 2 SCZ GWAS27 loci with a PP4 > 0.8 and significant MR association in SingleBrain and MiGA.

The circle represents expression quantitative trait loci (eQTLs) with a PP4 > 0.8. Triangles denote significant genes in the Mendelian randomization (MR) results, with the orientation of the triangle corresponding to the direction of the effect. COLOC, colocalization; MR, Mendelian randomization; MiGA, Microglia Genomic Atlas; MG, microglia; Ext, excitatory neuron; IN, inhibitory neuron; Ast, astrocyte; OD, oligodendrocyte; OPC, oligodendrocyte progenitor cell; End, endothelial cell.

Extended Data Fig. 3 ALS, BDP, and MS GWAS loci with a PP4 > 0.8 and significant MR association in SingleBrain and MiGA.

a–c, The circle represents expression quantitative trait loci (eQTLs) with a PP4 > 0.8. Triangles denote significant genes in the MR results, with the orientation of the triangle corresponding to the direction of the effect—(a) amyotrophic lateral sclerosis (ALS)30, (b) bipolar disorder (BPD)31 and (c) multiple sclerosis (MS)29. ALS, amyotrophic lateral sclerosis; BPD, bipolar disorder; MS, multiple sclerosis; COLOC, colocalization; MR, Mendelian randomization; MiGA, Microglia Genomic Atlas; MG, microglia; Ext, excitatory neuron; IN, inhibitory neuron; Ast, astrocyte; OD, oligodendrocyte; OPC, oligodendrocyte progenitor cell; End, endothelial cell.

Extended Data Fig. 4 AD loci from three separate AD GWAS with a PP4 > 0.8 and significant MR association in SingleBrain and MiGA.

a–c, The circle represents expression quantitative trait loci (eQTLs) with a PP4 > 0.8. Triangles denote significant genes in the MR results, with the orientation of the triangle corresponding to the direction of the effect24,25,32. COLOC, colocalization; MR, Mendelian randomization; MiGA, Microglia Genomic Atlas; MG, microglia; Ext, excitatory neuron; IN, inhibitory neuron; Ast, astrocyte; OD, oligodendrocyte; OPC, oligodendrocyte progenitor cell; End, endothelial cell.

Extended Data Fig. 5 PD (proxy and clinical cases) and PD (clinical only) GWAS28 loci with a PP4 > 0.8 and significant MR association in SingleBrain and MiGA.

a,b, The circle represents expression quantitative trait loci (eQTLs) with a PP4 > 0.8. Triangles denote significant genes in the MR results, with the orientation of the triangle corresponding to the direction of the effect. PD GWAS including proxy cases (a). PD GWAS with clinically defined cases (b). PD, Parkinson’s disease; COLOC, colocalization; MR, Mendelian randomization; MiGA, Microglia Genomic Atlas; MG, microglia; Ext, excitatory neuron; IN, inhibitory neuron; Ast, astrocyte; OD, oligodendrocyte; OPC, oligodendrocyte progenitor cell; End, endothelial cell.

Extended Data Fig. 6 AD GWAS23 loci colocalized with disease- and sex-specific eQTLs in SingleBrain (PP4 > 0.8).

The shape represents each type of expression quantitative trait loci (eQTLs) with PP4 greater than 0.8. Disease-specific eQTL (left) and sex-specific eQTL (right). MG, microglia; Ext, excitatory neuron; IN, inhibitory neuron; Ast, astrocyte; OD, oligodendrocyte; OPC, oligodendrocyte progenitor cell; End, endothelial cell.

Extended Data Fig. 7 Analysis of the PBX1 excitatory neuron eQTL with BPD GWAS NUF2 locus.

a, PBX1 expression is associated with the rs4442348 genotype, specifically in excitatory neurons. Residual expression was PEER-adjusted. The nominal P-value and β from the linear regression model in the brain cell type expression quantitative trait loci (eQTLs) analysis are indicated above the box plots (n = 983). The box plots show the median, the box spans from the first to the third quartiles, and the whiskers extend 1.5× interquartile range (IQR) from the box. b, Locus zoom and fine-mapping of the NUF2 BPD genome-wide association study (GWAS) and PBX1 excitatory neurons eQTL. Labels refer to lead single-nucleotide polymorphisms (SNPs) and fine-mapping SNPs with P < 1 × 10−4. Reported P values are two-sided and not corrected for multiple testing. The x-axis represents chromosomal position, and the y-axis represents the −log10(P value) for the association of variants with BPD and gene expression. SNPs are colored by the linkage disequilibrium (LD) with the lead GWAS SNP. Fine-mapped SNPs with PIP > 0.95 from GWAS and eQTL are presented under the plot. The red vertical line indicates the position of GWAS, eQTL lead SNPs, and fine-mapped SNPs with PIP > 0.95 and P < 1 × 10−4. c, Fine-mapping of the NUF2 locus and combination with the GWAS lead SNP, eQTL lead SNP, fine-mapping SNPs, credible SNPs (from colocalization analaysis), and consensus SNPs (defined as SNPs in LD [r2 = 1] with GWAS lead SNP). SNPs are colored by the LD with the lead GWAS SNP, overlapped with cell-type-specific enhancers and promoters were defined in ref. 19. Genomic plots (hg19) of lead SNPs and fine-mapping SNPs with P < 1 × 10−4 and epigenomic data from the microglia chromatin immunoprecipitation (ChIP)–seq and proximity ligation-assisted ChIP (PLAC)–seq junctions. BPD, bipolar disorder; Ext, excitatory neuron; IN, inhibitory neuron; OD, oligodendrocyte; Ast, astrocyte; OPC, oligodendrocyte progenitor cell; MG, microglia; End, endothelial cell.

Extended Data Fig. 8 Colocalization between SingleBrain eQTLs and GTEx brain cortex sQTLs.

a,b, All (a) AD and (b) PD GWAS loci with a colocalization PP4 > 0.8 at GTEx brain cortex sQTLs3 with SingleBrain eQTLs. The shape indicates eQTL and sQTL; opacity and size of the shape were scaled to PP4. Numbers next to the circles represent PP4 values.

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Jang, B., BP, K., Tokolyi, A. et al. A meta-analysis of single-nucleus expression quantitative trait loci linking genetic risk to brain disorders. Nat Genet (2026). https://doi.org/10.1038/s41588-026-02541-x

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