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Cell-type-specific effects of genetic variation on chromatin accessibility during human neuronal differentiation

Nature Neuroscience volume 24, pages 941–953 (2021)Cite this article

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Abstract

Common genetic risk for neuropsychiatric disorders is enriched in regulatory elements active during cortical neurogenesis. However, it remains poorly understood as to how these variants influence gene regulation. To model the functional impact of common genetic variation on the noncoding genome during human cortical development, we performed the assay for transposase accessible chromatin using sequencing (ATAC-seq) and analyzed chromatin accessibility quantitative trait loci (QTL) in cultured human neural progenitor cells and their differentiated neuronal progeny from 87 donors. We identified significant genetic effects on 988/1,839 neuron/progenitor regulatory elements, with highly cell-type and temporally specific effects. A subset (roughly 30%) of chromatin accessibility-QTL were also associated with changes in gene expression. Motif-disrupting alleles of transcriptional activators generally led to decreases in chromatin accessibility, whereas motif-disrupting alleles of repressors led to increases in chromatin accessibility. By integrating cell-type-specific chromatin accessibility-QTL and brain-relevant genome-wide association data, we were able to fine-map and identify regulatory mechanisms underlying noncoding neuropsychiatric disorder risk loci.

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Fig. 1: Profiling genome-wide chromatin accessibility in progenitors and neurons.
Fig. 2: caQTLs in progenitors and neurons.
Fig. 3: Fine-mapping and regulatory mechanism underlying eQTLs.
Fig. 4: ASCA.
Fig. 5: Cell-type-specificity of caQTLs.
Fig. 6: Prediction of disrupted TF binding due to genetic variation.
Fig. 7: Cell-type-specific caQTLs lead to regulatory mechanisms underlying GWAS loci.

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Data availability

Data generated in this paper (including metadata) can be accessed via dbGaP at accession number phs001958.v1.p1. The RNA-seq and genotype datasets used for fetal cortical eQTL analysis are available at dbGaP with accession number phs001900. REST ChIP–seq data in H1 embryonic stem cells and neurons differentiated from H1 cells are available via the ENCODE portal (https://www.encodeproject.org/) with the following identifiers: ENCSR000BTV and ENCSR000BHM.

Code availability

All code used in this paper is deposited on bitbucket at https://bitbucket.org/steinlabunc/celltypespecificcaqtls_wasp/src/master/.

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Acknowledgements

This work was supported by the National Institutes of Health (NIH) (grant nos. R00MH102357, U54EB020403, R01MH118349 and R01MH120125), Brain Research Foundation and NC TraCS Pilot funding to J.L.S. D.H.G. was supported by NIH (grant nos. R37 MH060233, R01 MH094714, UO1MH116489 and R01 MH110927). The following core facilities were used for this project: UNC Neuroscience Center Microscopy Core (P30NS045892), UNC Mammalian Genotyping Core, CGIBD Advanced Analytics Core (NIH grant no. P30 DK034987), UNC Flow Cytometry Core Facility, UNC Vector Core and UNC Research Computing. Additional core facilities used for this project were UCLA CFAR (5P30 AI028697) and the UCLA Neuroscience Genomics Core. We thank K.L. Mohlke for helpful comments. Adult caQTLs were supported by the PsychENCODE Consortium: grant nos. U01MH103392, U01MH103365, U01MH103346, U01MH103340, U01MH103339, R21MH109956, R21MH105881, R21MH105853, R21MH103877, R21MH102791, R01MH111721, R01MH110928, R01MH110927, R01MH110926, R01MH110921, R01MH110920, R01MH110905, R01MH109715, R01MH109677, R01MH105898, R01MH105898, R01MH094714, P50MH106934, U01MH116488, U01MH116487, U01MH116492, U01MH116489, U01MH116438, U01MH116441, U01MH116442, R01MH114911, R01MH114899, R01MH114901, R01MH117293, R01MH117291 and R01MH117292 awarded to: S. Akbarian (Icahn School of Medicine at Mount Sinai), G. Crawford (Duke University), S. Dracheva (Icahn School of Medicine at Mount Sinai), P. Farnham (University of Southern California), M. Gerstein (Yale University), D.H.G. (University of California, Los Angeles), F. Goes (Johns Hopkins University), T.M. Hyde (Lieber Institute for Brain Development), A. Jaffe (Lieber Institute for Brain Development), J.A. Knowles (University of Southern California), C. Liu (SUNY Upstate Medical University), D. Pinto (Icahn School of Medicine at Mount Sinai), P. Roussos (Icahn School of Medicine at Mount Sinai), S. Sanders (University of California, San Francisco), N. Sestan (Yale University), P. Sklar (Icahn School of Medicine at Mount Sinai), M. State (University of California, San Francisco), P. Sullivan (University of North Carolina), F. Vaccarino (Yale University), D. Weinberger (Lieber Institute for Brain Development), S. Weissman (Yale University), K. White (University of Chicago), J. Willsey (University of California, San Francisco) and P. Zandi (Johns Hopkins University. We acknowledge the ENCODE Consortium and the ENCODE production laboratory(ies) generating the particular dataset(s).

Author information

Authors and Affiliations

  1. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Dan Liang, Angela L. Elwell, Nil Aygün, Oleh Krupa, Justin M. Wolter, Felix A. Kyere, Michael J. Lafferty, Kerry E. Cheek, Kenan P. Courtney, Michael I. Love & Jason L. Stein

  2. UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Dan Liang, Angela L. Elwell, Nil Aygün, Oleh Krupa, Justin M. Wolter, Felix A. Kyere, Michael J. Lafferty, Kerry E. Cheek, Kenan P. Courtney & Jason L. Stein

  3. Neurogenetics Program, Department of Neurology, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA

    Marianna Yusupova, Luis de la Torre-Ubieta & Daniel H. Geschwind

  4. Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA

    Marianna Yusupova, Luis de la Torre-Ubieta & Daniel H. Geschwind

  5. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Marianna Yusupova, Luis de la Torre-Ubieta & Daniel H. Geschwind

  6. Duke Molecular Physiology Institute, Duke University, Durham, NC, USA

    Melanie E. Garrett & Allison Ashley-Koch

  7. Department of Medicine, Duke University, Durham, NC, USA

    Allison Ashley-Koch

  8. Center for Genomic and Computational Biology, Duke University, Durham, NC, USA

    Gregory E. Crawford

  9. Department of Pediatrics, Division of Medical Genetics, Duke University, Durham, NC, USA

    Gregory E. Crawford

  10. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Michael I. Love

  11. Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA

    Luis de la Torre-Ubieta & Daniel H. Geschwind

Authors
  1. Dan Liang
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  2. Angela L. Elwell
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  3. Nil Aygün
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  4. Oleh Krupa
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  5. Justin M. Wolter
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  6. Felix A. Kyere
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  7. Michael J. Lafferty
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  8. Kerry E. Cheek
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  9. Kenan P. Courtney
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  10. Marianna Yusupova
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  11. Melanie E. Garrett
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  12. Allison Ashley-Koch
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  13. Gregory E. Crawford
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  14. Michael I. Love
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  15. Luis de la Torre-Ubieta
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  16. Daniel H. Geschwind
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  17. Jason L. Stein
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Contributions

J.L.S., D.H.G. and L.T.U. conceived the study. J.L.S. directed and supervised the study. J.L.S. along with D.H.G. provided funding. A.L.E., K.E.C., K.P.C., M.Y., L.T.U. and J.L.S. cultured HNP cells. A.L.E. performed library preparation. M.J.L. preprocessed the RNA-seq data for eQTL. N.A. performed eQTL analysis. O.K. performed immunocytochemistry. O.K., J.M.W., F.A.K. and D.L. performed the functional validation assays. M.E.G., A.A.-K. and G.E.C. provided access to adult dlPFC caQTL data. M.I.L. aided in ASCA methodology. D.L. performed preprocessing, differential accessibility, caQTL, ASCA, colocalization and motif analyses. J.L.S. and D.L. wrote the paper. All authors commented on and approved the final version of the paper.

Corresponding author

Correspondence to Jason L. Stein.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Andrew Jaffe and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Flowchart for cell culture and preprocessing of ATAC-seq data.

a, Flowchart of cell culture for 17 rounds. b, The FACS gates for sorting EGFP + neurons. c, Images of immunofluorescence for cell markers in progenitor cultures. Immunolabeling experiments were repeated in at least 10 unique donor cell lines with similar results. The scale bar presents 100 μm. d, Images of immunofluorescence for cell markers in 8-week differentiated cultures. Immunolabeling experiments were repeated in at least 10 unique donor cell lines with similar results. The scale bar presents 100 μm. e, Box plot for total sequence depth (forward reads and reverse reads), unique read number (forward reads and reverse reads), duplicate rate, mitochondrial duplicate rate, TSS enrichment and the fraction of reads in called peak regions (FRiP score) in neurons (N = 61) and progenitors (N = 76) compared to previously published data (N_GZ = 3 biologically independent samples with 3-4 replicates for each sample, N_CP = 3 biologically independent samples with 3 replicates for each sample)15. The center of the box is median of the data, the bounds of the box are 25th percentile and 75th percentile of the data, and the whisker boundary is 1.5 times the IQR. Maximum and minimum are the maximum and minimum of the data.

Extended Data Fig. 2 ATAC-seq data QC.

a, Peak calling versus library sequencing depth. We observed a slower rise in the number of new peaks called after 15 millions filtered read pairs. This indicates a reasonable balance between read depth and number of peaks called using an average of 14 million read pairs after filtering in our samples. b, Insert size histograms for 3 randomly selected neuron and progenitor samples. c, PCA plot for ATAC-seq data (N = 137) before batch correction (left) and after batch correction (right), colored by sorter. We corrected normalized reads within ATAC-seq peaks in neurons by sorter locations. Then, we corrected normalized reads within ATAC-seq peaks in neurons and progenitors by cell culture round. d, Correlations of batch corrected normalized reads across donors and within donors. Correlations within donors was significantly higher than correlations across donors in progenitor (n = 15). Correlations within donors were higher than correlations across donors in neurons (n = 4), but not significant (p = 0.07). P values are estimated by two-sided wilcoxon tests. The center of the box is median of the data, the bounds of the box are 25th percentile and 75th percentile of the data, and the whisker boundary is 1.5 times the IQR. Maximum and minimum are the maximum and minimum of the data. e, Correlations between PC1 to PC10 from normalized reads in neurons with known technical and biological factors. f, Correlations between PC1 to PC10 from batch correction normalized reads in progenitors with known technical and biological factors.

Extended Data Fig. 3 Annotating differentially accessible peaks during neuronal differentiation.

a, Gene ontology (GO) enrichment of differentially accessible peaks at the TSS. Progenitor peaks (left) and neuron peaks (right) showed enrichment for GO terms related to proliferation and differentiation, as expected. b, TFs with significantly differentially enriched conserved binding sites in differentially accessible peaks. The statistical test identifies TFs likely involved in neural progenitor proliferation and maintenance (progenitorTFs; top) or neurogenesis and maturation (neuronTFs; bottom). The top 30 significantly enriched TFs were shown in this figure, and the full list can be found in Supplementary Table 2. Within progenitorTFs, we found TFs previously characterized to have key roles for neural stem cell renewal and reprogramming, such as SOX2101,102, and those known to be required for the maintenance of stem cells in cortex, such as NR2F1, ETV5, and SP2103,104,105. Within neuronTFs, NEUROG2 and LMX1A were identified, which are known to drive neuronal differentiation106,107, as well as TFs shown to induce neuronal identity from fibroblasts, including ASCL2 and the POU family39. NeuronTFs also included CUX1/2, a marker for layer II-III neurons61,108 and other laminar markers such as TBR1 and FOXP1. c, Schematic of known functions for selected progenitorTFs and neuronTFs.

Extended Data Fig. 4 Features of caQTLs.

a, Flowchart for caQTL data analysis. b, PCA plot for ATAC-seq data on sex chromosomes (chrX and chrY), colored by sex from genotype data, showing sex could be called using ATAC-seq data. c, MDS plot for genotype data of HapMap3 and donors in this study, colored by populations from HapMap3 data. ASW: African ancestry in Southwest USA; CEU: Utah residents with Northern and Western European ancestry from the CEPH collection; CHB: Han Chinese in Beijing, China; CHD: Chinese in Metropolitan Denver, Colorado; GIH: Gujarati Indians in Houston, Texas; JPT: Japanese in Tokyo, Japan; LWK: Luhya in Webuye, Kenya; MEX: Mexican ancestry in Los Angeles, California; MKK: Maasai in Kinyawa, Kenya; TSI: Toscans in Italy; YRI: Yoruba in Ibadan, Nigeria. d, Neuron and progenitor caPeaks enrichment at epigenetically annotated regulatory elements from fetal brain (Epigenetics Roadmap ID = E081). e, Comparison of percent variance explained (r2) for shared neuron/progenitor caQTLs and fetal brain eQTLs (subset to the same sample size). P values are estimated by two-sided paired student-t tests. The center of the box is median of the data, the bounds of the box are 25th percentile and 75th percentile of the data, and the whisker boundary is 1.5 times the IQR. Maximum and minimum are the maximum and minimum of the data.

Extended Data Fig. 5 Examples of fine-mapping and regulatory mechanisms underlying eQTLs.

a, Colocalization of a progenitor-specific caQTL and fetal cortical eQTL for ETFDH. b, caQTL for rs11544037 and the labeled peak in progenitor (N = 76). P-values are estimated by a mixed linear effects model using a two-sided test (Methods). c, eQTL of ETFDH in bulk fetal cortex (N = 235). P-values are estimated by a mixed linear effects model using a two-sided test (Methods). d, The expression of TFs whose motifs are disrupted by rs1154403722 (LFC = -0.32, FDR = 7.55e-18)26. e, The motif Logo of RAD21, where the red box shows the position disrupted by rs11544037. Schematic cartoon of mechanisms for rs11544037 regulating chromatin accessibility and gene expression. f, Luciferase signals for alleles of rs11544037 in progenitors (N = 8). P value is from two-sided paired t-tests. g, Co-localization of a progenitor-specific caQTL and eQTL for FGF1. h, CaQTL for rs11960262 and the labeled peak in progenitor (N = 76). P-values are estimated by a mixed linear effects model using a two-sided test (Methods). i, eQTL of ETFDH in progenitors (N = 85). P-values are estimated by a mixed linear effects model using a two-sided test (Methods). j, The expression of TFs in which motifs are disrupted by rs11960262. k, The motif Logo of EGR1, where the red box shows the position disrupted by rs11960262. Schematic cartoon of mechanisms for rs11960262 regulating chromatin accessibility and gene expression. (For box plots in (b-c), (f) and (h-i), the center of the box is the median, the bounds of the box are 25th percentile and 75th percentile of the data, and the whisker boundary is 1.5 times the IQR. Maximum and minimum are the maximum and minimum of the data.).

Extended Data Fig. 6 Features of ASCA.

a, Density plot for caPeak length from shared caQTLs and ASCA, and from peaks only significant in ASCA in neurons (top) and progenitors (bottom). P values are estimated by two-sided Student’s t-tests. b, The neuron ASCA (caSNP: rs62332390; caPeak: chr4:148,441,611-148,46,300; P values are estimated by the negative binomial generalized linear models from DESeq2 using a two-sided test62) is not a significant caQTL (N = 61; P values are estimated by the mixed linear model using a two sided test) in neurons because the caPeak was very wide (4,689 bp) and only the region near the ASCA SNP shows an association with genotype. c, The neuron ASCA (caSNP:rs77191441; caPeak:chr5:116,571,961-116,576,710; P values are estimated by the negative binomial generalized linear models from DESeq2 using a two-sided test62) is not a significant caQTL (N = 61; P values are estimated by the mixed linear effects model with a two-sided test) in neurons due to low minor allele frequency leading to less power to detect a caQTL. d, ASCA between rs185220 (see Fig. 3) and chromatin accessibility in progenitors (left) and neurons (right). P-values are estimated by the negative binomial generalized linear models from DESeq2 using a two-sided test62. (For box plots in (b) and (c), the center of the box is the median, the bounds of the box are 25th percentile and 75th percentile of the data, and the whisker boundary is 1.5 times the IQR. Maximum and minimum are the maximum and minimum of the data.).

Extended Data Fig. 7 Comparison to adult dorsolateral prefrontal cortex (DLPFC) caQTLs.

a, Shared accessible peaks overlap at epigenetically annotated regulatory elements from different tissues. Accessible peak bp percentage overlapped with epigenetically annotated regulatory elements. From left to right, tissues ordered by bp percentage overlap with enhancers and promoters. Shared peaks overlap with both adult and fetal regulatory elements. b, PCA plot for read counts from shared peaks in adult DLPFC, neurons and progenitors. c, Correlations of effect sizes for significant neuron caQTLs and the same SNP-Peak pairs in adult DLPFC (left). Correlations of effect sizes for significant progenitor caQTLs and the same SNP-Peak pairs in adult DLPFC (right).

Extended Data Fig. 8 An example of a neuron-specific caQTL leading to regulatory mechanisms underlying GWAS loci.

a, Numbers of colocalizations between ASCA and GWAS loci. b, The neuron-specific significant caQTL (caSNP: rs9930307; caPeak: chr16: 9,805,221-9,805,420) co-localized with schizophrenia GWAS locus (index SNP: rs7191183). c, Box plot for the caQTL (left, N = 61; P values are estimated by the mixed linear effects model using a two-sided test) and ASCA (right) (caSNP: rs9930307; caPeak: chr16: 9,805,221-9,805,420; P values are estimated by the negative binomial generalized linear models from DESeq2 using a two-sided test62). d, The expression of TFs in which motifs are disrupted by rs9930307. e, The motif logo of TP53 and the position disrupted by rs9930307. f, The box plot for luciferase signal for alleles of rs9930307 in progenitors (N = 8). P value is from two-sided paired student-t tests. (For box plots in (c) and (f), the center of the box is median of the data, the bounds of the box are 25th percentile and 75th percentile of the data, and the whisker boundary is 1.5 times the IQR. Maximum and minimum are the maximum and minimum of the data.).

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Liang, D., Elwell, A.L., Aygün, N. et al. Cell-type-specific effects of genetic variation on chromatin accessibility during human neuronal differentiation. Nat Neurosci 24, 941–953 (2021). https://doi.org/10.1038/s41593-021-00858-w

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