A compendium of promoter-centered long-range chromatin interactions in the human genome

Jung, Inkyung; Schmitt, Anthony; Diao, Yarui; Lee, Andrew; Liu, Tristin; Yang, Dongchan; Tan, Catherine; Eom, Junghyun; Chan, Marilynn; Chee, Sora; Chiang, Zachary; Kim, Changyoun; Masliah, Eliezer; Barr, Cathy L.; Li, Bin; Kuan, Samantha; Kim, Dongsup; Ren, Bing

doi:10.1038/s41588-019-0494-8

Cited by 304 publications

(374 citation statements)

References 40 publications

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“…This may be especially useful for cross-population prediction, which is sensitive to misspecification of causal SNPs due to LD differences between populations 56,57 . Third, the proximal pairs of coding and non-coding fine-mapped SNPs that we identified ( Supplementary Table 22) may aid efforts to link SNPs to genes [42][43][44] . Fourth, SNPs that were fine-mapped for multiple genetically uncorrelated traits may shed light on shared biological pathways 58 .…”

Section: Discussionmentioning

confidence: 99%

“…Second, in the N=337K analysis, we determined that the union of PolyFun + SuSiE 95% credible sets (defined as sets with probability >0.95 of including ≥1 causal SNP 22 ; see above) was 23% smaller than the union of SuSiE 95% credible sets (median reduction) ( Supplementary Table 21), consistent with simulations ( Supplementary Table 4). Third, we searched for pairs of fine-mapped SNPs within 1Mb of each other where exactly one of the SNPs is coding, which can aid in linking regulatory variants to target genes [42][43][44] , and identified 490 such pairs ( Supplementary Table 22). Fourth, we compared the magnitude of the posterior mean and posterior standard deviation of causal effect sizes, which can inform the applicability of fine-mapping results to polygenic risk scores.…”

Section: Functionally Informed Fine-mapping Of 47 Complex Traits In Tmentioning

confidence: 99%

“…Second, in the N=337K analysis, we determined that the union of PolyFun + SuSiE 95% credible sets (defined as sets with probability >0.95 of including ≥1 causal SNP 22 ; see above) was 23% smaller than the union of SuSiE 95% credible sets (median reduction) ( Supplementary Table 21), consistent with simulations ( Supplementary Table 4). Third, we searched for pairs of finemapped SNPs within 500kb of each other where exactly one of the SNPs is coding, which can aid in linking regulatory variants to target genes 42,43 , and identified 490 such pairs ( Supplementary Table 22). Fourth, we verified that the functional enrichments of fine-mapped SNPs remained qualitatively similar when using PolyFun + SuSiE instead of SuSiE ( Supplementary Figure 2, Supplementary Table 23) and when defined using all MAF≥0.001 SNPs ( Supplementary Figure 3, Supplementary Table 24) or only lowfrequency and rare SNPs (0.05>MAF≥0.001) ( Supplementary Figure 4, Supplementary Table 25).…”

Section: Functionally Informed Fine-mapping Of 47 Complex Traits In Tmentioning

confidence: 99%

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Functionally-informed fine-mapping and polygenic localization of complex trait heritability

Weissbrod

Hormozdiari

Benner

et al. 2019

Preprint

162

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Fine-mapping aims to identify causal variants impacting complex traits. Several recent methods improve fine-mapping accuracy by prioritizing variants in enriched functional annotations. However, these methods can only use information at genome-wide significant loci (and/or a small number of functional annotations), severely limiting the benefit of functional data. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy using genome-wide functional data for a broad set of coding, conserved, regulatory and LD-related annotations. PolyFun prioritizes variants in enriched functional annotations by specifying prior causal probabilities for fine-mapping methods such as SuSiE or FINEMAP, employing special procedures to ensure robustness to model misspecification and winner's curse. In simulations, PolyFun + SuSiE and PolyFun + FINEMAP were well-calibrated and identified >20% more variants with posterior causal probability >0.95 than their non-functionally informed counterparts (and >33% more fine-mapped variants than previous functionally-informed fine-mapping methods). In analyses of 47 UK Biobank traits (average N=317K), PolyFun + SuSiE identified 3,025 fine-mapped varianttrait pairs with posterior causal probability >0.95, a >32% improvement vs. SuSiE; 223 variants were finemapped for multiple genetically uncorrelated traits, indicating pervasive pleiotropy. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 25 (hair color) to 3,400 (height) to 550,000 (chronotype). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures.

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Section: Discussionmentioning

confidence: 99%

Section: Functionally Informed Fine-mapping Of 47 Complex Traits In Tmentioning

confidence: 99%

“…Second, in the N=337K analysis, we determined that the union of PolyFun + SuSiE 95% credible sets (defined as sets with probability >0.95 of including ≥1 causal SNP 22 ; see above) was 23% smaller than the union of SuSiE 95% credible sets (median reduction) ( Supplementary Table 21), consistent with simulations ( Supplementary Table 4). Third, we searched for pairs of finemapped SNPs within 500kb of each other where exactly one of the SNPs is coding, which can aid in linking regulatory variants to target genes 42,43 , and identified 490 such pairs ( Supplementary Table 22). Fourth, we verified that the functional enrichments of fine-mapped SNPs remained qualitatively similar when using PolyFun + SuSiE instead of SuSiE ( Supplementary Figure 2, Supplementary Table 23) and when defined using all MAF≥0.001 SNPs ( Supplementary Figure 3, Supplementary Table 24) or only lowfrequency and rare SNPs (0.05>MAF≥0.001) ( Supplementary Figure 4, Supplementary Table 25).…”

Section: Functionally Informed Fine-mapping Of 47 Complex Traits In Tmentioning

confidence: 99%

See 1 more Smart Citation

Functionally-informed fine-mapping and polygenic localization of complex trait heritability

Weissbrod

Hormozdiari

Benner

et al. 2019

Preprint

162

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“…Our study also highlights the biological importance of the long-range chromatin contacts in lineage-specific gene expression. A recent study showed that functional enhancer-promoter pairs predominantly locate in very close genomic distances 29 , despite the fact that a huge number of cis-regulatory elements and interactions between promoters and distal elements have been annotated 1, 2, 50, 51 . We show here that lineage-specific expression of many genes may be dependent on long-range (>100 kb) enhancer-promoter contacts anchored by CTCF binding at the promoters.…”

Section: Discussionmentioning

confidence: 99%

CTCF Promotes Long-range Enhancer-promoter Interactions and Lineage-specific Gene Expression in Mammalian Cells

Kubo¹,

Ishii²,

Xiong

et al. 2020

Preprint

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23 24 [All datasets have been deposited to GEO, with accession number GSE94452, and can be 25 accessed here]. 26 3 Abstract: 27 Topologically associating domains (TAD) and insulated neighborhoods (INs) have been 28 proposed to constrain enhancer-promoter communications to enable cell-type specific 29 transcription programs, but recent studies show that disruption of TADs and INs resulted in 30 relatively mild changes in gene expression profiles. To better understand the role of 31 chromatin architecture in dynamic enhancer-promoter contacts and lineage-specific gene 32 expression, we have utilized the auxin-inducible degron system to acutely deplete CTCF, a 33 key factor involved in TADs and IN formation, in mouse embryonic stem cells (mESCs) and 34 examined chromatin architecture and gene regulation during neural differentiation. We find 35 that while CTCF depletion leads to global weakening of TAD boundaries and loss of INs, only 36 a minor fraction of enhancer-promoter contacts are lost, affecting a small subset of genes. 37 The CTCF-dependent enhancer-promoter contacts tend to be long-range, spanning hundreds 38 of kilobases, and are established directly by CTCF binding to promoters. Disruption of CTCF 39 binding at the promoter reduces enhancer-promoter contacts and transcription, while 40 artificial tethering of CTCF to the promoter restores the enhancer-promoter contacts and 41 gene activation. Genome-wide analysis of CTCF binding and gene expression across multiple 42 mouse tissues suggests that CTCF-dependent promoter-enhancer contacts may regulate 43 4 expression of additional mouse genes, particularly those expressed in the brain. Our results 44 uncover both CTCF-dependent and independent enhancer-promoter contacts, and highlight a 45 distinct role for CTCF in promoting enhancer-promoter contacts and gene activation in 46 addition to its insulator function. 47 48 5 Introduction: 49Transcriptional regulation in mammalian cells is orchestrated by cis-regulatory elements that include 50 promoters, enhancers, insulators and other less well characterized sequences 1,2 . Large-scale 51 projects such as ENCODE have annotated millions of candidate cis-regulatory elements in the 52 human genome and genomes of other mammalian species 3-5 . A majority of these candidate 53 regulatory elements are located far from transcription start sites(i.e. promoters), display tissue and 54 cell-type specific chromatin accessibility, and likely act as enhancers to regulate cell-type specific 55 gene expression. Enhancers are frequently found to be positioned close to their target gene 56 promoters in 3D space at the time of gene activation, suggesting a role for the chromatin 57 architecture in gene regulation 6,7 . Indeed, artificially induced spatial proximity between enhancers 58 and promoters has been shown to lead to gene activation 8,9 . Insulators, on the other hand, act to 59 block enhancer-promoter contacts to prevent ectopic gene activation 10-12 . Clearly, in-depth 60 knowledge of the chromatin architecture in each...

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“…The genome-wide study of three-dimensional (3D) organization has revealed its intrinsically association with gene regulation and cell function [1][2][3] . Genome-wide sequencing approaches such as Hi-C 4 , GAM 5 and SPRITE 6 have shown that the genome is organized hierarchically, from specific promoter-enhancer contacts, to topological associating domains (TADs), to broader compartments of open and closed chromatin (compartments A and B, respectively), up to whole chromosome territories [4][5][6][7] .…”

Section: Introductionmentioning

confidence: 99%

Cell-type specialization in the brain is encoded by specific long-range chromatin topologies

Winick-Ng

Kukalev

Harabula

et al. 2020

Preprint

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Neurons and oligodendrocytes are terminally differentiated cells that perform highly specialized functions, which depend on cascades of gene activation and repression to retain homeostatic control over a lifespan. Gene expression is regulated by threedimensional (3D) genome organisation, from local levels of chromatin compaction to the organisation of topological domains and chromosome compartments. Whereas our understanding of 3D genome architecture has vastly increased in the past decade, it remains difficult to study specialized cells in their native environment without disturbing their activity. To develop the application of Genome Architecture Mapping (GAM) in small numbers of specialized cells in complex tissues, we combined GAM with immunoselection. We applied immunoGAM to map the genome architecture of specific cell populations in the juvenile/adult mouse brain: dopaminergic neurons (DNs) from the midbrain, pyramidal glutamatergic neurons (PGNs) from the hippocampus, and oligodendrocyte lineage cells (OLGs) from the cortex. We integrate 3D genome organisation with single-cell transcriptomics data, and find specific chromatin structures that relate with cell-type specific patterns of gene expression. We discover abundant changes in compartment organisation, especially a strengthening of heterochromatin compartments which establish strong contacts spanning tens of megabases, especially in brain cells. These compartments contain olfactory and taste receptor genes, which are de-repressed in a subpopulation of PGNs with molecular signatures of long-term potentiation (LTP). We also show extensive reorganisation of topological domains where activation of neuronal or oligodendrocyte genes coincides with formation of new TAD borders.Finally, we discover loss of TAD organisation, or 'TAD melting', at long (>1Mb) neuronal genes when they are most highly expressed. Our work shows that the 3D 3 organisation of the genome is highly cell-type specific in terminally differentiated cells of the brain, and essential to better understand brain-specific mechanisms of gene regulation.

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A compendium of promoter-centered long-range chromatin interactions in the human genome

Cited by 304 publications

References 40 publications

Functionally-informed fine-mapping and polygenic localization of complex trait heritability

Functionally-informed fine-mapping and polygenic localization of complex trait heritability

CTCF Promotes Long-range Enhancer-promoter Interactions and Lineage-specific Gene Expression in Mammalian Cells

Cell-type specialization in the brain is encoded by specific long-range chromatin topologies

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