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Population-level variation in enhancer expression identifies disease mechanisms in the human brain

Nature Genetics volume 54, pages 1493–1503 (2022)Cite this article

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Abstract

Identification of risk variants for neuropsychiatric diseases within enhancers underscores the importance of understanding population-level variation in enhancer function in the human brain. Besides regulating tissue-specific and cell-type-specific transcription of target genes, enhancers themselves can be transcribed. By jointly analyzing large-scale cell-type-specific transcriptome and regulome data, we cataloged 30,795 neuronal and 23,265 non-neuronal candidate transcribed enhancers. Examination of the transcriptome in 1,382 brain samples identified robust expression of transcribed enhancers. We explored gene–enhancer coordination and found that enhancer-linked genes are strongly implicated in neuropsychiatric disease. We identified expression quantitative trait loci (eQTLs) for both genes and enhancers and found that enhancer eQTLs mediate a substantial fraction of neuropsychiatric trait heritability. Inclusion of enhancer eQTLs in transcriptome-wide association studies enhanced functional interpretation of disease loci. Overall, our study characterizes the gene–enhancer regulome and genetic mechanisms in the human cortex in both healthy and diseased states.

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Fig. 1: Catalog of cell-type-specific TEns in the human cortex.
Fig. 2: TEn expression captures cell-type-specific enhancer function.
Fig. 3: Gene–enhancer expression coordination.
Fig. 4: Genetic effects of enhancer expression.
Fig. 5: SCZ TWAS.

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Data availability

Processed data (epigenomic annotations, TEns, differential analysis result, read count matrix of TEns and genes, gene–enhancer links, eQTL summary, and TWAS weights) have been deposited on the Synapse platform at https://doi.org/10.7303/syn25716684. Download requires registering for a Synapse account (https://www.synapse.org/#!RegisterAccount:0).

The multi-omics cohort, including ATAC-seq, RNA-seq, H3K4me3 ChIP-seq, H3K27me3 ChIP-seq, H3K27ac ChIP-seq and Hi-C data, is available through the AD Knowledge Portal Super Ager EpiMap Study at https://adknowledgeportal.synapse.org/Explore/Studies/DetailsPage?Study=syn25672226. The AD Knowledge Portal is a platform for accessing data, analyses and tools generated by the AMP AD Target Discovery Program and other National Institute on Aging (NIA)-supported programs to enable open-science practices and accelerate translational learning. The data, analyses and tools are shared early in the research cycle without a publication embargo on secondary use. Data are available for general research use according to the requirements for data access and data attribution detailed at https://adknowledgeportal.org/DataAccess/Instructions.

The CMC data are available through the CMC Knowledge Portal. The CMC investigators are committed to the release of data and analysis results, with the anticipation that data sharing in a rapid and transparent manner will speed the pace of research to the benefit of the greater research community. Data are available for general research use according to following requirements for data access and data attribution detailed at https://www.synapse.org//#!Synapse:syn2759792/wiki/197282.

Code availability

The TEn identification pipeline is available at https://zenodo.org/record/6845955 (ref. 109). Further information and requests for reagents may be directed to the corresponding author.

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Acknowledgements

We thank the computational resources and staff expertise provided by the Scientific Computing group of the Icahn School of Medicine at Mount Sinai. The CommonMind data sets were generated as part of the CommonMind Consortium supported by funding from Takeda Pharmaceuticals Company Limited; F. Hoffman-La Roche Ltd; and NIH grants R01MH085542, R01MH093725, P50MH066392, P50MH080405, R01MH097276, RO1-MH-075916, P50M096891, P50MH084053S1, R37MH057881, AG02219, AG05138, MH06692, R01MH110921, R01MH109677, R01MH109897, U01MH103392, U01MH116442, project ZIC MH002903 and contract HHSN271201300031C through the NIMH Intramural Research Program (IRP). Brain tissue for the study was obtained from the following brain bank collections: the Mount Sinai/JJ Peters VA Medical Center NIH Brain and Tissue Repository, the University of Pennsylvania Alzheimer’s Disease Core Center, the University of Pittsburgh Brain Tissue Donation Program, and the NIMH Human Brain Collection Core. CMC leadership: P.R., J.D.B., A.C., S.A., V.H., B.D., D.A.L., R.G., C.-G.H., E.D., M.A.P., S.K.S., S.M., B.K.L. and F.J.M. This work is supported by the NIA through NIH grants R01-AG067025 (to P.R. and V.H.), R01-AG065582 (to P.R. and V.H.) and R01-AG050986 (to P.R.); by the NIMH through NIH grants R01-MH110921 (to P.R.), U01-MH116442 (to P.R. and V.H.), R01-MH125246 (to P.R.), R01-MH106056 (to P.R. and K.J.B.), R01-MH109897 (to P.R. and K.J.B.) and R01-MH121074 (to K.J.B.); and by the Veterans Affairs Merit grant BX002395 (to P.R.). P.D. was supported in part by NARSAD Young Investigator Grant 29683 from the Brain & Behavior Research Foundation. G.E.H. was supported in part by NARSAD Young Investigator Grant 26313 from the Brain & Behavior Research Foundation. J.B. was supported in part by NARSAD Young Investigator Grant 27209 from the Brain & Behavior Research Foundation.

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Author notes

  1. Kristen J. Brennand

    Present address: Department of Psychiatry, Yale University, New Haven, CT, USA

Authors and Affiliations

  1. Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Pengfei Dong, Gabriel E. Hoffman, Jaroslav Bendl, Samir Rahman, Biao Zeng, James M. Vicari, Kiran Girdhar, Mariana Amaro, Shan Jiang, Steven P. Kleopoulos, Yixian Ma, Yungil Kim, Georgios Voloudakis, John F. Fullard & Panos Roussos

  2. Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Pengfei Dong, Pasha Apontes, Jaroslav Bendl, Samir Rahman, Michael B. Fernando, Biao Zeng, James M. Vicari, Wen Zhang, Kiran Girdhar, Kayla G. Townsley, Ruth Misir, Kristen J. Brennand, Vahram Haroutunian, Georgios Voloudakis, John F. Fullard & Panos Roussos

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

    Pengfei Dong, Gabriel E. Hoffman, Pasha Apontes, Jaroslav Bendl, Samir Rahman, Biao Zeng, James M. Vicari, Wen Zhang, Kiran Girdhar, Ruth Misir, John F. Fullard & Panos Roussos

  4. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Pengfei Dong, Gabriel E. Hoffman, Pasha Apontes, Jaroslav Bendl, Samir Rahman, Biao Zeng, James M. Vicari, Wen Zhang, Kiran Girdhar, Ruth Misir, Andrew Chess, Attila Gulyás-Kovács, Bibi Kassim, Joseph D. Buxbaum, Laura Sloofman, Lizette Couto, Mariana Amaro, Marina Iskhakova, Michael Breen, Olivia Devillers, Schahram Akbarian, Shan Jiang, Steven P. Kleopoulos, Yixian Ma, Yungil Kim, Kristen J. Brennand, Vahram Haroutunian, Georgios Voloudakis, John F. Fullard & Panos Roussos

  5. Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Pengfei Dong, Gabriel E. Hoffman, Pasha Apontes, Jaroslav Bendl, Samir Rahman, Biao Zeng, James M. Vicari, Wen Zhang, Kiran Girdhar, Ruth Misir, Eva Xia, Laura Sloofman, Yungil Kim, John F. Fullard & Panos Roussos

  6. Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, New York, NY, USA

    Pasha Apontes, Vahram Haroutunian & Panos Roussos

  7. Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Michael B. Fernando, Kayla G. Townsley, Kristen J. Brennand & Vahram Haroutunian

  8. Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Michael B. Fernando & Kristen J. Brennand

  9. Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA

    Panos Roussos

  10. Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Andrew Chess

  11. Translational Neuroscience Laboratory, McLean Hospital, Belmont, MA, USA

    Sabina Berretta

  12. Human Brain Collection Core, NIMH-IRP, Bethesda, MD, USA

    Ajeet Mandal, Barbara K. Lipska, Pavan K. Auluck & Stefano Marenco

  13. Human Genetics Branch, NIMH-IRP, Bethesda, MD, USA

    Francis McMahon

  14. Sage Bionetworks, Seattle, WA, USA

    Kelsey S. Montgomery, Mette A. Peters & Solveig K. Sieberts

  15. Translational Neuropsychiatric Research Unit, Department of Psychiatry and Neuroscience, The Sidney Kimmel Medical College, Philadelphia, PA, USA

    Chang-Gyu Hahn

  16. Neuropsychiatry Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Raquel Gur

  17. Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA

    Jiebiao Wang

  18. Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Bernie Devlin, David A. Lewis & Lambertus Klei

  19. Laboratory of Neurogenomic Biomarkers, Centre for Integrative Biology (CIBIO), University of Trento, Trento, Italy

    Enrico Domenici, Michele Filosi & Roberto Visintainer

  20. Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA

    Douglas M. Ruderfer & Lide Han

Authors
  1. Pengfei Dong
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  2. Gabriel E. Hoffman
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  3. Pasha Apontes
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  4. Jaroslav Bendl
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  5. Samir Rahman
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  6. Michael B. Fernando
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  7. Biao Zeng
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  8. James M. Vicari
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  9. Wen Zhang
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  10. Kiran Girdhar
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  11. Kayla G. Townsley
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  12. Ruth Misir
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  13. Kristen J. Brennand
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  14. Vahram Haroutunian
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  15. Georgios Voloudakis
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  16. John F. Fullard
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  17. Panos Roussos
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Consortia

the CommonMind Consortium

  • Pengfei Dong
  • , Gabriel E. Hoffman
  • , Jaroslav Bendl
  • , Biao Zeng
  • , Kiran Girdhar
  • , Vahram Haroutunian
  • , Georgios Voloudakis
  • , John F. Fullard
  • , Panos Roussos
  • , Andrew Chess
  • , Attila Gulyás-Kovács
  • , Bibi Kassim
  • , Eva Xia
  • , Joseph D. Buxbaum
  • , Laura Sloofman
  • , Lizette Couto
  • , Mariana Amaro
  • , Marina Iskhakova
  • , Michael Breen
  • , Olivia Devillers
  • , Schahram Akbarian
  • , Shan Jiang
  • , Steven P. Kleopoulos
  • , Yixian Ma
  • , Yungil Kim
  • , Sabina Berretta
  • , Ajeet Mandal
  • , Barbara K. Lipska
  • , Francis McMahon
  • , Pavan K. Auluck
  • , Stefano Marenco
  • , Kelsey S. Montgomery
  • , Mette A. Peters
  • , Solveig K. Sieberts
  • , Chang-Gyu Hahn
  • , Raquel Gur
  • , Jiebiao Wang
  • , Bernie Devlin
  • , David A. Lewis
  • , Lambertus Klei
  • , Enrico Domenici
  • , Michele Filosi
  • , Roberto Visintainer
  • , Douglas M. Ruderfer
  •  & Lide Han

Contributions

P.R. conceived of and designed the project. J.F.F. and P.R. designed experimental strategies for epigenome profiling of human postmortem tissue. J.F.F. prepared nuclei and performed FANS. J.F.F. and R.M. generated ATAC-seq data. P.A. generated the ChIP-seq and multi-omics RNA-seq data. V.H. dissected and provided multi-omics brain specimens. S.R. generated Hi-C data. S.R., J.M.V., M.B.F., K.G.T. and K.J.B. performed the CRISPR interference experiments. P.D. and P.R. designed analytical strategies. J.B., K.G. and P.D. conducted initial bioinformatics, sample processing and quality control for the multi-omics cohort. G.E.H. and W.Z. conducted initial bioinformatics, sample processing and quality control for the CMC cohort. P.D. developed the computational scheme and performed the downstream analysis. P.D. and G.V. performed the TWAS analysis. B.Z. performed the MMQTL analysis. P.D. and P.R. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Panos Roussos.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 TEns identification.

a, The distribution of deconvolved RNA-seq cell type distribution for each sample (Nneuron = 47, Nnon-neuron = 46). Box plot indicates median, interquartile range (IQR) and 1.5 × IQR. b, Jaccard index between the Multi-omics peaks and previous reports. cell-type-specific ATAC-seq/H3K4me3/H3K27ac peaks were compared to our previous reports of the corresponding assays9,110. H3K27me3 was compared to Roadmap H3K27me3 peaks23. PFC indicates the prefrontal cortex. c, Empirical density distribution of FANTOM5 enhancer size; based on the curve, we chose 500 bp as the TEn size. d, Feature importance heatmap from random forest models. e, Distribution of TEn numbers per super-enhancer in the two cell types (Nneuron = 2,049, Nnon-neuron = 1,946). Box plots as in a. f, Distance to the nearest TEns for every TEn within or outside of super-enhancer regions (Nneuron = 29,555, Nnon-neuron = 23,260, P < 10−16 for both cell types, two-sided Wilcoxon test). Box plots as in a. g, Epigenomic profiles of expressed TEns, and promoters of protein-coding genes and lincRNAs. The line represents the mean, and the shadow indicates a 95% confidence interval.

Extended Data Fig. 2 TEns identification methods compare.

a, Overlap between our model(RF) and logistic regression (logit), ChromHMM +OCR (ChromHMM), and the OCR only (OCR) method in neuronal and non-neural cells (Methods). b, Strand-specific CAGE tags average profile for the shared and methods-specific enhancers. In all three comparisons, our model-specific enhancers are enriched for CAGE tags, while the logit-, ChromHMM-, and OCR-specific are depleted of CAGE tags. The radar plots show typical enhancer-related signals including H3K4me3, H3K27ac, H3K27me3, super-enhancer, and loop anchor occupancy of OCR only specific, our model-specific, and shared enhancers of the two methods in neuronal c, and non-neuronal d, cells. The value within parentheses indicates the odds ratio (OR) and P value between OCR only specific and the rest (one-sided Fisher exact test).

Extended Data Fig. 3 Differential expression/activity between neuronal and non-neuronal cells.

a, ChromHMM identified 6 chromatin states including EnhA (active enhancer), TssA (active promoters), TssBiv (bivalent promoters), TssFlnk (promoter flanking region), ReprPC (polycomb repression region), and Quies (other regions). b, Comparing the cell-type-specific Chromatin states with the roadmap DLPFC result. c, Jaccard index between neuronal and non-neuronal chromatin states. Compared to active promoters (TssA), and polycomb repressed regions (ReprPC), active enhancers (EnhA) are remarkably more different between cell types. d, Overlap of expressed TEns between neuronal and non-neuronal cells. e, π1 statistics of differential activity between the two cell types of different molecular markers. f, Violin plot shows the variance explained by different factors for the five markers (NATACSeq = 98, NTEns = Ngene = 93, NH3K27ac = 96, NH3K4me3 = 96). Box plot indicates median, interquartile range (IQR), and 1.5 × IQR.

Extended Data Fig. 4 GWAS association with different classes of enhancers.

a. Stratified LD score regression of different classes of enhancers across different classes of traits. A positive coefficient signifies enrichment in heritability (per base enrichment, mean ± standard error).’·’: Nominally significant (P < 0.05);’+’: significant after FDR (Benjamini & Hochberg) correction (FDR < 0.05). b, coverage (Mb) and numbers (k) of different classes of enhancers. c, Stratified LD score regression of cell-type-specific enhancers defined by TEns, the intersection of differential H3K27ac and noncoding OCR (Diff H3K27ac ∩ OCR), and the intersection of differential noncoding OCR and H3K27ac (Diff OCR ∩ H3K27ac) across different classes of traits as in a. d, coverage (Mb), and numbers (k) of the three types of enhancers.

Extended Data Fig. 5 enhancer-gene link.

a, the expression level of different classes of genes and enhancers (Nintergeric_enhancer = 4,626, Nintronic_enhancer = 37,358, NlincRNA = 2,915, Nprotein_coding_gene = 16,468). Box plot indicates median, interquartile range (IQR), and 1.5 × IQR. b, Pairwise Spearman correlation coefficient (SCC) between samples of different classes of genes/enhancers of discovery vs discovery (DD), discovery vs replicate (DR), and replicate vs replicate (RR) (Nintergeric_enhancer = 4,080, Nintronic_enhancer = 36,493, NlincRNA = 2,665, Nprotein_coding_gene = 16,175). Box plots as in a. c, Distribution of gene FPKM (fragments per kilobase of transcript per million mapped reads) and counts for linked enhancers. Linked genes have a significantly higher gene expression (FPKM, two-sided Wilcoxon test, P < 10−16). Box plots as in a. d, Distribution of enhancer CPM and for counts linked genes. Linked enhancers do not have a higher expression (CPM, two-sided Wilcoxon test, P = 0.96). Box plots as in a. e, Distance between enhancers to genes of linked group and background (Nlink = 35,964, Nbackground = 241,040). f, Distance between enhancers to genes of linked group and background, physically-overlapped gene-enhancer pairs were excluded (Nlink = 22,920, Nbackground = 220,062). Box plots as in a. g, Neuronal vs non-neuronal t statistics for linked and non-linked enhancers. Linked enhancer has a significantly higher value (two-sided KS test, P < 10−16).

Extended Data Fig. 6 enhancer-gene link.

a, Scatter plot of pairwise Spearman correlation (SCC) between linked enhancers and genes for the Discovery set (x-axis) and Replicate set (y-axis), the two sets of correlations are highly consistent (SCC = 0.79, P < 10−16, two-sided Spearman correlation test). b, Neuropsychiatric diseases and behavioral traits, neurological diseases, and non-brain related trait enrichment with different classes of genes (determined by MAGMA). For BD and SCZ, we included different GWAS versions including PGC2 SCZ1 (Ncase = 36,989, Ncontrol = 113,075), PGC3 SCZ34 (Ncase = 69,369, Ncontrol = 236,642), PGC2 BD111 (Ncase = 29,764, Ncontrol = 169,118), PGC3 BD5 (Ncase = 41,917, Ncontrol = 371,549). c, Pairwise comparison of the coefficients of significant traits for enhancer-linked neuronal genes (one-sided t-test). The color of the heatmap exhibits the one-sided t-test significance (row vs column).’·’: Nominally significant (P < 0.05);’+’: significant after FDR (Benjamini & Hochberg) correction (FDR < 0.05).

Extended Data Fig. 7 gene and enhancer eQTL.

a, Percentage of genes/enhancers that are cis-heritable (GCTA nominal P < 0.05 and cis-heritability > 0). b, Overlap of cis-heritable genes between ACC and DLPFC. c, Overlap of cis-heritable enhancers between ACC and DLPFC. d, Dot plots illustrate the first two genetic ancestry principal components (PCs) for individuals reported (report) to have European ancestry and the individuals selected (select) to have European ancestry based on sd to the center. e, The replication of eQTL from the discovery set in the replicate set across different P value cutoffs for the discovery set. π1 values (proportion of true positive p values) for the discovery set significant eQTL in our replicate eQTLs. SCC values are the Spearman correlation coefficients of significant eQTL effect sizes between the two sets. f, The percentage of genes/enhancers that have significant eQTL are cis-heritable across different P value cutoffs. Overlap of the cis heritable transcript with the transcript with eQTL for genes g, and h, enhancer.

Extended Data Fig. 8 consistent genetic effects between linked genes and enhancers.

a, The distribution of genomic distances from eSNPs to the TSSs for different classes of transcripts. b, The replication of reported eQTL in our analysis. π1 values (proportion of true positive P values) for reported significant GTEx eQTL in our gene eQTLs. SCC values are the Spearman correlation coefficients of significant eQTL effect sizes between GTEx eQTL and corresponding pairs in our data. The bar color represents the number of unique genes used. c, for both physically overlapping and non-overlapping pairs, we subsampled from the non-linked pairs to generate backgrounds that have similar levels of enhancer-TSS distance distributions compared to linked pairs (numbers in the parenthesis indicate the number of pairs). Box plot indicates median, interquartile range (IQR) and 1.5 × IQR. d, The allelic genetic effect between gene and target enhancers considering the gene-enhancer physically-overlapping and enhancer-TSS effects. The background is controlled for enhancer-TSS distance.

Extended Data Fig. 9 quantile-quantile plot of GWAS P values.

Quantile–quantile plot of GWAS p values across different GWAS traits. EeQTL specific, eQTL specific, and shared SNPs are shown in comparison with genome-wide SNPs. GWAS SNPs were binarily annotated using SNPs within r2 > 0.8 of the eSNP.

Extended Data Fig. 10 GWAS association.

a, Stratified LD score regression of neuronal and non-neuronal enhancer/gene eQTL across different classes of traits. The gene/enhancer eQTLs are assigned to Neuronal and non-Neuronal groups based on the differential expression status between the two cell types. A positive coefficient signifies enrichment in heritability (per base enrichment, mean ± standard error).’·’: Nominally significant (P < 0.05);’+’: significant after FDR (Benjamini & Hochberg) correction (FDR < 0.05). b, Gene TWAS Z scores compared to published reports52,53. SCC represents the Spearman correlation coefficient (ρ) (all P < 10−16, two-sided Spearman correlation test). c, For different types of transcripts, the TWAS Z scores between the two brain regions. SCC represents the Spearman correlation coefficient (ρ)(all P < 10−16, two-sided Spearman correlation test). d, Aligned Manhattan plots of SCZ GWAS and EeQTLs at the enh41216 locus generated by LocusCompare. SNPs are colored by LD (r2) with the lead EeQTL (rs3247233).

Supplementary information

Supplementary Information

Supplementary Methods and Supplementary Fig. 1.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–8.

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Dong, P., Hoffman, G.E., Apontes, P. et al. Population-level variation in enhancer expression identifies disease mechanisms in the human brain. Nat Genet 54, 1493–1503 (2022). https://doi.org/10.1038/s41588-022-01170-4

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