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[Preprint]. 2023 Mar 2:2023.03.02.530826.
doi: 10.1101/2023.03.02.530826.

Genetic regulation of cell-type specific chromatin accessibility shapes the etiology of brain diseases

Biao Zeng  1   2   3   4 , Jaroslav Bendl  1   2   3   4 , Chengyu Deng  5   6 , Donghoon Lee  1   2   3   4 , Ruth Misir  1   2   3   4 , Sarah M Reach  1   2   3   4 , Steven P Kleopoulos  1   2   3   4 , Pavan Auluck  7 , Stefano Marenco  7 , David A Lewis  8 , Vahram Haroutunian  2   4   9   10 , Nadav Ahituv  5   6 , John F Fullard  1   2   3   4 , Gabriel E Hoffman  1   2   3   4 , Panos Roussos  1   2   3   4   10
Affiliations

Genetic regulation of cell-type specific chromatin accessibility shapes the etiology of brain diseases

Biao Zeng et al. bioRxiv. .

Update in

Abstract

Nucleotide variants in cell type-specific gene regulatory elements in the human brain are major risk factors of human disease. We measured chromatin accessibility in sorted neurons and glia from 1,932 samples of human postmortem brain and identified 34,539 open chromatin regions with chromatin accessibility quantitative trait loci (caQTL). Only 10.4% of caQTL are shared between neurons and glia, supporting the cell type specificity of genetic regulation of the brain regulome. Incorporating allele specific chromatin accessibility improves statistical fine-mapping and refines molecular mechanisms underlying disease risk. Using massively parallel reporter assays in induced excitatory neurons, we screened 19,893 brain QTLs, identifying the functional impact of 476 regulatory variants. Combined, this comprehensive resource captures variation in the human brain regulome and provides novel insights into brain disease etiology.

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Conflict of interest statement

Competing interests N.A. is the cofounder and on the scientific advisory board of Regel Therapeutics and receives funding from BioMarin Pharmaceutical Incorporated.

Figures

Figure 1:
Figure 1:. Cell-type specific regulation of chromatin accessibility.
A) Experimental workflow including brain dissection, tissue homogenization, fluorescence-activated nuclear sorting (FANS) using the NeuN marker followed by ATAC-seq. B) Counts of donors and tissue samples for two cohorts, four brain regions and two cell types. Venn diagram shows donor count for each cohort along with a very small overlap. C) Number of OCRs with detected caQTLs compared to their sample size for each of 8 data subsets. Color indicates cell type, shape indicates brain region, and encompassing ellipse indicates cohort. D) Replication rate of caQTL’s discovered in each data subset in caQTL results from brain homogenate (19) using Storey’s π1 statistic (22). E) Replicate rate of caQTL’s across each pair of data subsets using Storey’s π1 statistic followed by hierarchical clustering. caQTL discovery was performed in the datasets along the columns and replication rate was measured in the datasets along the rows. F) Multivariate Bayesian meta-analysis identifies shared and cell type specific genetic regulatory effects on OCRs shared across cell types.
Figure 2:
Figure 2:. Characterizing cell type specific regulatory variants
A) Number of OCRs with detected caQTLs for cell type specific OCRs, and OCR shared across cell types. For the shared OCRs, the caQTL can have either a shared or cell type specific effect. B) Enrichment of lead caQTL variants within OCR discovered for 6 cell type populations from single cell ATAC-seq (23). C) Estimated effect size for detected caQTLs using an expanding search window up to 500 kb shows decay of effect size with distance. Inset shows larger effect size for variants within versus outside the OCR (p < 2.2e-16 by Wilcoxon test). D) Enrichment of fine-mapped variants within each posterior probability interval for disrupting a TFBS motif compared to the background set of tested SNPs not in the 95% credible set. E) Enrichment of fine-mapped variants in the 95% credible set for disrupting a TFBS motif shown for the top 30 transcription factors for each cell type. For each TF, its cell type specificity is shown based on the enrichment of its corresponding motif in neuronal- and glial-specific OCRs in the brain open chromatin atlas (16).
Figure 3:
Figure 3:. Allele specific chromatin accessibility
A) Diagram illustrating chromatin accessibility along the genome (top) and allele specific chromatin accessibility (ASCA) inferred by allelic imbalance. B) ASCA was inferred by testing the null hypothesis of equal fraction of alternative and reference alleles. Here, power to detect weak effects increases with the read depth. Red points indicate genome-wide FDR < 5%. C) Enrichment of significant ASCA variants in OCRs detected from single nucleus ATAC-seq (23). D) Relationship between the regression slope β estimated from caQTL regression and the allelic ratio from ASCA analysis. E) Comparison of the size of the 95% credible set from caQTL and merged (caQTL and ASCA) analyses. Each point represents an OCR. F) Comparison of fine-mapping results between caQTL and merged (caQTL and ASCA) analyses showing median size of credible sets (top) and number of OCRs with a single credible variant (bottom) for neurons and glia. G) Results for neuronal OCR using caQTL regression method, ASCA, and merged (caQTL and ASCA) analyses. Using the merged analysis reduces the 95% credible set to a single SNP, rs11202088, in the OCR that is predicted to disrupt an ERG3 binding site. Variants in the 95% credible set are colored in red. OCR locations are shown and the target of the caQTL is colored red.
Figure 4:
Figure 4:. Shared genetic regulation of multiple molecular features
A) Number of genes in colocalized gene-OCR pairs for each cell type. B) OCR-gene pairs with shared genetic regulation are enriched for enhancer-promoter interaction. C) OCRs and their lead caQTL variant are enriched for being located in the same cell type specific topologically associated domain (TAD). D) Fine-mapped caQTL variants are enriched for fine-mapped eQTL variants from multiple cell types across a range of posterior probability thresholds. E) caQTL detected in glia for OCR at chr14:99,822,700–99,823,524 colocalizes with the eQTL signal for EML1 detected in brain homogenate (18). OCR locations are shown and the target of the caQTL is colored red.
Figure 5:
Figure 5:. Integration of caQTLs with disease risk
A) Enrichment of caQTL variants in the 95% credible set for risk variants computed by LD-score regression (29). B) Counts of colocalized regions based on signals from caQTL, eQTL and GWAS for neuropsychiatric and neurodegenerative disease. Counts are stratified based on which signals colocalize. C) Colocalization between caQTL, eQTL and GWAS signals where the caQTL signal is cell type specific and posterior probability from MOLOC is > 0.9. Colors indicate cell type, whether the OCR is shared between cell types, and whether an enhancer-promoter (E-P) link is identified. For each gene-OCR pair, the distance between them and the number of intervening genes is shown. D) Colocalization of signals from eQTL, caQTL from neurons and risk for major depressive disorder (MDD) supports shared genetic regulation. Variants in the 95% credible set are shown in red. Joint statistical fine-mapping across three traits identified the SNP rs3764512 as the top candidate. This SNP is located within an OCR at the transcription start site of RAB27B.
Figure 6.
Figure 6.. Massively parallel reporter assay.
A) Diagram of massively parallel reporter assay (MPRA) with synthesized DNA constructs with two alleles inserted into a lentivirus MPRA vector. Vectors were transduced into human induced pluripotent stem cell (hiPSC)-derived excitatory NGN2 neurons, and the sequencing readout of DNA and RNA of barcodes was used to estimate differential allelic effects. B) Constructs with significant enhancer effects are enriched in open chromatin regions in multiple neuronal subtypes, in addition to non-neuronal cell types (23, 36). Enrichments with FDR < 5% are indicated with ‘#’. C) Volcano plot of allelic fold changes for candidates causal SNPs. SNPs with FDR < 10% are indicated in red, and larger points indicate SNPs with higher precision (i.e. lower standard error). D) Magnitude of allelic effects are shown for four sets of SNPs. Candidate causal SNPs have larger allelic effect magnitude than a SNPS 300–1,200 bp way. Candidate causal SNPs in OCRs have larger allelic effects than SNP in OCRs but with no detected eQTL. E) Regression coefficients showing relationship between magnitude of allelic effect and multiple SNP annotations (left). Color indicates the coefficient estimate. Associations with FDR < 5% are indicated by ‘#’. The percent of variation in allelic effect magnitude explained by each annotation is shown from the same regression models (right). F) Effect size estimates for rs3764512 comparing the G allele to the T allele shows consistency in direction.
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