Global view of enhancer-promoter interactome in human cells

Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data

Single-cell DNA methylation data has become increasingly abundant and has uncovered many genes with a positive correlation between expression and promoter methylation, challenging the common dogma based on bulk data. However, computational tools for analyzing single-cell methylome data are lagging far behind. A number of tasks, including cell type calling and integration with transcriptome data, requires the construction of a robust gene activity matrix as the prerequisite but challenging task. The advent of multi-omics data enables measurement of both DNA methylation and gene expression for the same single cells. Although such data is rather sparse, they are sufficient to train supervised models that capture the complex relationship between DNA methylation and gene expression and predict gene activities at single-cell level. Here, we present methylome association by predictive linkage to expression (MAPLE), a computational framework that learns the association between DNA methylation and expression using both gene- and cell-dependent statistical features. Using multiple data sets generated with different experimental protocols, we show that using predicted gene activity values significantly improves several analysis tasks, including clustering, cell type identification, and integration with transcriptome data. Application of MAPLE revealed several interesting biological insights into the relationship between methylation and gene expression, including asymmetric importance of methylation signals around transcription start site for predicting gene expression, and increased predictive power of methylation signals in promoters located outside CpG islands and shores. With the rapid accumulation of single-cell epigenomics data, MAPLE provides a general framework for integrating such data with transcriptome data.

Prediction of enhancer-promoter interactions using the cross-cell type information and domain adversarial neural network

BACKGROUND

Enhancer-promoter interactions (EPIs) play key roles in transcriptional regulation and disease progression. Although several computational methods have been developed to predict such interactions, their performances are not satisfactory when training and testing data from different cell lines. Currently, it is still unclear what extent a across cell line prediction can be made based on sequence-level information.

RESULTS

In this work, we present a novel Sequence-based method (called SEPT) to predict the enhancer-promoter interactions in new cell line by using the cross-cell information and Transfer learning. SEPT first learns the features of enhancer and promoter from DNA sequences with convolutional neural network (CNN), then designing the gradient reversal layer of transfer learning to reduce the cell line specific features meanwhile retaining the features associated with EPIs. When the locations of enhancers and promoters are provided in new cell line, SEPT can successfully recognize EPIs in this new cell line based on labeled data of other cell lines. The experiment results show that SEPT can effectively learn the latent import EPIs-related features between cell lines and achieves the best prediction performance in terms of AUC (the area under the receiver operating curves).

CONCLUSIONS

SEPT is an effective method for predicting the EPIs in new cell line. Domain adversarial architecture of transfer learning used in SEPT can learn the latent EPIs shared features among cell lines from all other existing labeled data. It can be expected that SEPT will be of interest to researchers concerned with biological interaction prediction.

Discover novel disease-associated genes based on regulatory networks of long-range chromatin interactions

Identifying genes and non-coding genetic variants that are genetically associated with complex diseases and the underlying mechanisms is one of the most important questions in functional genomics. Due to the limited statistical power and the lack of mechanistic modeling, traditional genome-wide association studies (GWAS) is restricted to fully address this question. Based on multi-omics data integration, cell-type specific regulatory networks can be built to improve GWAS analysis. In this study, we developed a new computational infrastructure, APRIL, to incorporate 3D chromatin interactions into regulatory network construction, which can extend the networks to include long-range cis-regulatory links between non-coding GWAS SNPs and target genes. Combinatorial transcription factors that co-regulate groups of genes are also inferred to further expand the networks with trans-regulation. A suite of machine learning predictions and statistical tests are incorporated in APRIL to predict novel disease-associated genes based on the expanded regulatory networks. Important features of non-coding regulatory elements and genetic variants are prioritized in network-based predictions, providing systems-level insights on the mechanisms of transcriptional dysregulation associated with complex diseases.

Aberrant methylation underlies insulin gene expression in human insulinoma

Human insulinomas are rare, benign, slowly proliferating, insulin-producing beta cell tumors that provide a molecular "recipe" or "roadmap" for pathways that control human beta cell regeneration. An earlier study revealed abnormal methylation in the imprinted p15.5-p15.4 region of chromosome 11, known to be abnormally methylated in another disorder of expanded beta cell mass and function: the focal variant of congenital hyperinsulinism. Here, we compare deep DNA methylome sequencing on 19 human insulinomas, and five sets of normal beta cells. We find a remarkably consistent, abnormal methylation pattern in insulinomas. The findings suggest that abnormal insulin (INS) promoter methylation and altered transcription factor expression create alternative drivers of INS expression, replacing canonical PDX1-driven beta cell specification with a pathological, looping, distal enhancer-based form of transcriptional regulation. Finally, NFaT transcription factors, rather than the canonical PDX1 enhancer complex, are predicted to drive INS transactivation.

A practical guide for DNase-seq data analysis: from data management to common applications

Deoxyribonuclease I (DNase I)-hypersensitive site sequencing (DNase-seq) has been widely used to determine chromatin accessibility and its underlying regulatory lexicon. However, exploring DNase-seq data requires sophisticated downstream bioinformatics analyses. In this study, we first review computational methods for all of the major steps in DNase-seq data analysis, including experimental design, quality control, read alignment, peak calling, annotation of cis-regulatory elements, genomic footprinting and visualization. The challenges associated with each step are highlighted. Next, we provide a practical guideline and a computational pipeline for DNase-seq data analysis by integrating some of these tools. We also discuss the competing techniques and the potential applications of this pipeline for the analysis of analogous experimental data. Finally, we discuss the integration of DNase-seq with other functional genomics techniques.

EPIP: a novel approach for condition-specific enhancer-promoter interaction prediction

MOTIVATION

The identification of enhancer-promoter interactions (EPIs), especially condition-specific ones, is important for the study of gene transcriptional regulation. Existing experimental approaches for EPI identification are still expensive, and available computational methods either do not consider or have low performance in predicting condition-specific EPIs.

RESULTS

We developed a novel computational method called EPIP to reliably predict EPIs, especially condition-specific ones. EPIP is capable of predicting interactions in samples with limited data as well as in samples with abundant data. Tested on more than eight cell lines, EPIP reliably identifies EPIs, with an average area under the receiver operating characteristic curve of 0.95 and an average area under the precision-recall curve of 0.73. Tested on condition-specific EPIPs, EPIP correctly identified 99.26% of them. Compared with two recently developed methods, EPIP outperforms them with a better accuracy.

AVAILABILITY AND IMPLEMENTATION

The EPIP tool is freely available at http://www.cs.ucf.edu/˜xiaoman/EPIP/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Diverse noncoding mutations contribute to deregulation of cis-regulatory landscape in pediatric cancers

Interpreting the function of noncoding mutations in cancer genomes remains a major challenge. Here, we developed a computational framework to identify putative causal noncoding mutations of all classes by joint analysis of mutation and gene expression data. We identified thousands of SNVs/small indels and structural variants as putative causal mutations in five major pediatric cancers. We experimentally validated the oncogenic role of CHD4 overexpression via enhancer hijacking in B-ALL. We observed a general exclusivity of coding and noncoding mutations affecting the same genes and pathways. We showed that integrated mutation profiles can help define novel patient subtypes with different clinical outcomes. Our study introduces a general strategy to systematically identify and characterize the full spectrum of noncoding mutations in cancers.

Transcriptional regulatory network controlling the ontogeny of hematopoietic stem cells

In this study from Gao et al., the authors performed RNA-seq and histone mark ChIP-seq to define the transcriptomes and epigenomes of cells representing key developmental stages of HSC ontogeny in mice. Using a novel computational algorithm, target inference via physical connection (TIPC), they constructed developmental stage-specific transcriptional regulatory networks by linking enhancers and predicted bound transcription factors to their target promoters, thus providing a useful resource for uncovering regulators of HSC formation.

Hematopoietic stem cell (HSC) ontogeny is accompanied by dynamic changes in gene regulatory networks. We performed RNA-seq and histone mark ChIP-seq to define the transcriptomes and epigenomes of cells representing key developmental stages of HSC ontogeny in mice. The five populations analyzed were embryonic day 10.5 (E10.5) endothelium and hemogenic endothelium from the major arteries, an enriched population of prehematopoietic stem cells (pre-HSCs), fetal liver HSCs, and adult bone marrow HSCs. Using epigenetic signatures, we identified enhancers for each developmental stage. Only 12% of enhancers are primed, and 78% are active, suggesting the vast majority of enhancers are established de novo without prior priming in earlier stages. We constructed developmental stage-specific transcriptional regulatory networks by linking enhancers and predicted bound transcription factors to their target promoters using a novel computational algorithm, target inference via physical connection (TIPC). TIPC predicted known transcriptional regulators for the endothelial-to-hematopoietic transition, validating our overall approach, and identified putative novel transcription factors, including the broadly expressed transcription factors SP3 and MAZ. Finally, we validated a role for SP3 and MAZ in the formation of hemogenic endothelium. Our data and computational analyses provide a useful resource for uncovering regulators of HSC formation.

Integrative computational epigenomics to build data-driven gene regulation hypotheses

BACKGROUND

Diseases are complex phenotypes often arising as an emergent property of a non-linear network of genetic and epigenetic interactions. To translate this resulting state into a causal relationship with a subset of regulatory features, many experiments deploy an array of laboratory assays from multiple modalities. Often, each of these resulting datasets is large, heterogeneous, and noisy. Thus, it is non-trivial to unify these complex datasets into an interpretable phenotype. Although recent methods address this problem with varying degrees of success, they are constrained by their scopes or limitations. Therefore, an important gap in the field is the lack of a universal data harmonizer with the capability to arbitrarily integrate multi-modal datasets.

RESULTS

In this review, we perform a critical analysis of methods with the explicit aim of harmonizing data, as opposed to case-specific integration. This revealed that matrix factorization, latent variable analysis, and deep learning are potent strategies. Finally, we describe the properties of an ideal universal data harmonization framework.

CONCLUSIONS

A sufficiently advanced universal harmonizer has major medical implications, such as (i) identifying dysregulated biological pathways responsible for a disease is a powerful diagnostic tool; (2) investigating these pathways further allows the biological community to better understand a disease's mechanisms; and (3) precision medicine also benefits from developments in this area, particularly in the context of the growing field of selective epigenome editing, which can suppress or induce a desired phenotype.

Noncoding SNPs associated with increased GDF15 levels located in a metformin-activated enhancer region upstream of GDF15

Aim: GDF15 levels are a biomarker for metformin use. We performed the functional annotation of noncoding genome-wide association study (GWAS) SNPs for GDF15 levels and the Genotype-Tissue Expression (GTEx)-expression quantitative trait loci (eQTLs) for GDF15 expression within metformin-activated enhancers around GDF15. Materials & methods: These enhancers were identified using chromatin immunoprecipitation followed by sequencing data for active (H3K27ac) and silenced (H3K27me3) histone marks on human hepatocytes treated with metformin, Encyclopedia of DNA Elements data and cis-regulatory elements assignment tools. Results: The GWAS lead SNP rs888663, the SNP rs62122429 associated with GDF15 levels in the Outcome Reduction with Initial Glargine Intervention trial, and the GTEx-expression quantitative trait locus rs4808791 for GDF15 expression in whole blood are located in a metformin-activated enhancer upstream of GDF15 and tightly linked in Europeans and East Asians. Conclusion: Noncoding variation within a metformin-activated enhancer may increase GDF15 expression and help to predict GDF15 levels.

EnhancerAtlas 2.0: an updated resource with enhancer annotation in 586 tissue/cell types across nine species

Enhancers are distal cis-regulatory elements that activate the transcription of their target genes. They regulate a wide range of important biological functions and processes, including embryogenesis, development, and homeostasis. As more and more large-scale technologies were developed for enhancer identification, a comprehensive database is highly desirable for enhancer annotation based on various genome-wide profiling datasets across different species. Here, we present an updated database EnhancerAtlas 2.0 (http://www.enhanceratlas.org/indexv2.php), covering 586 tissue/cell types that include a large number of normal tissues, cancer cell lines, and cells at different development stages across nine species. Overall, the database contains 13 494 603 enhancers, which were obtained from 16 055 datasets using 12 high-throughput experiment methods (e.g. H3K4me1/H3K27ac, DNase-seq/ATAC-seq, P300, POLR2A, CAGE, ChIA-PET, GRO-seq, STARR-seq and MPRA). The updated version is a huge expansion of the first version, which only contains the enhancers in human cells. In addition, we predicted enhancer–target gene relationships in human, mouse and fly. Finally, the users can search enhancers and enhancer–target gene relationships through five user-friendly, interactive modules. We believe the new annotation of enhancers in EnhancerAtlas 2.0 will facilitate users to perform useful functional analysis of enhancers in various genomes.

Loss-of-function tolerance of enhancers in the human genome

Previous studies have surveyed the potential impact of loss-of-function (LoF) variants and identified LoF-tolerant protein-coding genes. However, the tolerance of human genomes to losing enhancers has not yet been evaluated. Here we present the catalog of LoF-tolerant enhancers using structural variants from whole-genome sequences. Using a conservative approach, we estimate that individual human genomes possess at least 28 LoF-tolerant enhancers on average. We assessed the properties of LoF-tolerant enhancers in a unified regulatory network constructed by integrating tissue-specific enhancers and gene-gene interactions. We find that LoF-tolerant enhancers tend to be more tissue-specific and regulate fewer and more dispensable genes relative to other enhancers. They are enriched in immune-related cells while enhancers with low LoF-tolerance are enriched in kidney and brain/neuronal stem cells. We developed a supervised learning approach to predict the LoF-tolerance of all enhancers, which achieved an area under the receiver operating characteristics curve (AUROC) of 98%. We predict 3,519 more enhancers would be likely tolerant to LoF and 129 enhancers that would have low LoF-tolerance. Our predictions are supported by a known set of disease enhancers and novel deletions from PacBio sequencing. The LoF-tolerance scores provided here will serve as an important reference for disease studies.

Enhancers are elements where transcription factors bind and regulate the expression of protein-coding genes. Although multiple previous studies have focused on which genes can tolerate loss-of-function (LoF), none has systematically evaluated the tolerance of all enhancers in the human genome to LoF. Individual studies have shown a broad range of phenotypic effects of enhancer LoF. The phenotypic effects of enhancer LoF likely fall into a spectrum where deletion of LoF-tolerant enhancers would not elicit substantial phenotypic impact, while some enhancers are likely to cause fitness defects when deleted. Here we report a systematic computational approach that uses machine learning and properties of enhancers in a unified human regulatory network with tissue-specific annotations to predict the LoF-tolerance of all enhancers identified in the human genome. The LoF-tolerance scores of enhancers provided in this study can significantly facilitate the interpretation and prioritization of non-coding sequence variants for disease and functional studies.

Contribution of unfixed transposable element insertions to human regulatory variation

Thousands of unfixed transposable element (TE) insertions segregate in the human population, but little is known about their impact on genome function. Recently, a few studies associated unfixed TE insertions to mRNA levels of adjacent genes, but the biological significance of these associations, their replicability across cell types and the mechanisms by which they may regulate genes remain largely unknown. Here, we performed a TE-expression QTL analysis of 444 lymphoblastoid cell lines (LCL) and 289 induced pluripotent stem cells using a newly developed set of genotypes for 2743 polymorphic TE insertions. We identified 211 and 176 TE-eQTL acting in cis in each respective cell type. Approximately 18% were shared across cell types with strongly correlated effects. Furthermore, analysis of chromatin accessibility QTL in a subset of the LCL suggests that unfixed TEs often modulate the activity of enhancers and other distal regulatory DNA elements, which tend to lose accessibility when a TE inserts within them. We also document a case of an unfixed TE likely influencing gene expression at the post-transcriptional level. Our study points to broad and diverse cis-regulatory effects of unfixed TEs in the human population and underscores their plausible contribution to phenotypic variation. This article is part of a discussion meeting issue 'Crossroads between transposons and gene regulation'.

Enhancer-gene maps in the human and zebrafish genomes using evolutionary linkage conservation

The spatiotemporal expression of genes is controlled by enhancer sequences that bind transcription factors. Identifying the target genes of enhancers remains difficult because enhancers regulate gene expression over long genomic distances. To address this, we used an evolutionary approach to build two genome-wide maps of predicted enhancer-gene associations in the human and zebrafish genomes. Evolutionary conserved sequences were linked to their predicted target genes using PEGASUS, a bioinformatics method that relies on evolutionary conservation of synteny. The analysis of these maps revealed that the number of predicted enhancers linked to a gene correlate with its expression breadth. Comparison of both maps identified hundreds of putative vertebrate ancestral regulatory relationships from which we could determine that predicted enhancer-gene distances scale with genome size despite strong positional conservation. The two maps represent a resource for further studies, including the prioritization of sequence variants in whole genome sequence of patients affected by genetic diseases.

Transcriptional network dynamics during the progression of pluripotency revealed by integrative statistical learning

The developmental potential of cells, termed pluripotency, is highly dynamic and progresses through a continuum of naive, formative and primed states. Pluripotency progression of mouse embryonic stem cells (ESCs) from naive to formative and primed state is governed by transcription factors (TFs) and their target genes. Genomic techniques have uncovered a multitude of TF binding sites in ESCs, yet a major challenge lies in identifying target genes from functional binding sites and reconstructing dynamic transcriptional networks underlying pluripotency progression. Here, we integrated time-resolved ‘trans-omic’ datasets together with TF binding profiles and chromatin conformation data to identify target genes of a panel of TFs. Our analyses revealed that naive TF target genes are more likely to be TFs themselves than those of formative TFs, suggesting denser hierarchies among naive TFs. We also discovered that formative TF target genes are marked by permissive epigenomic signatures in the naive state, indicating that they are poised for expression prior to the initiation of pluripotency transition to the formative state. Finally, our reconstructed transcriptional networks pinpointed the precise timing from naive to formative pluripotency progression and enabled the spatiotemporal mapping of differentiating ESCs to their in vivo counterparts in developing embryos.

Family-based whole-genome sequencing identifies compound heterozygous protein-coding and noncoding mutations in tetralogy of Fallot

Tetralogy of Fallot (TOF) is one of most serious cyanotic congenital heart disease (CHD) and the prevalence is estimated to be 1 in 3000 live births worldwide. Though multiple studies have found genetic variants as risk factors for TOF, they could only explain a small fraction of the pathogenesis. Here, we performed whole genome sequencing (WGS) for 6 individuals derived from 2 families to evaluate pathogenic mutations located in both coding and noncoding regions. We characterized the annotated deleterious coding mutations and impaired noncoding mutations in regulatory elements by various data analysis. Additionally, functional assays were conducted to validate function regulatory elements and noncoding mutations. Interestingly, a compound heterozygous pattern with pathogenic coding and noncoding mutations was identified in probands. In proband 1, biallelic mutations (g.139409115A > T, encoding p.Asn685Ile; g.139444949C > A) in NOTCH1 exon and its regulatory element were detected. In vitro experiments revealed that the regulatory element acted as a silencer and the noncoding mutation decreased the expression of NOTCH1. In proband 2, we also found compound heterozygous mutations (g. 216235029C > T, encoding p.Val2281Met; g. 216525154A > C) which potentially regulated the function of FN1 gene. In summary, our study firstly reported an instance of newly identified noncoding mutation in regulatory element within the compound heterozygous pattern in TOF. The results provided a deeper understanding of TOF genetic architectures.

Exploring 3D chromatin contacts in gene regulation: The evolution of approaches for the identification of functional enhancer-promoter interaction

Mechanisms underlying gene regulation are key to understand how multicellular organisms with various cell types develop from the same genetic blueprint. Dynamic interactions between enhancers and genes are revealed to play central roles in controlling gene transcription, but the determinants to link functional enhancer-promoter pairs remain elusive. A major challenge is the lack of reliable approach to detect and verify functional enhancer-promoter interactions (EPIs). In this review, we summarized the current methods for detecting EPIs and described how developing techniques facilitate the identification of EPI through assessing the merits and drawbacks of these methods. We also reviewed recent state-of-art EPI prediction methods in terms of their rationale, data usage and characterization. Furthermore, we briefly discussed the evolved strategies for validating functional EPIs.

Integrative prediction of gene expression with chromatin accessibility and conformation data

BACKGROUND

Enhancers play a fundamental role in orchestrating cell state and development. Although several methods have been developed to identify enhancers, linking them to their target genes is still an open problem. Several theories have been proposed on the functional mechanisms of enhancers, which triggered the development of various methods to infer promoter-enhancer interactions (PEIs). The advancement of high-throughput techniques describing the three-dimensional organization of the chromatin, paved the way to pinpoint long-range PEIs. Here we investigated whether including PEIs in computational models for the prediction of gene expression improves performance and interpretability.

RESULTS

We have extended our [Formula: see text] framework to include DNA contacts deduced from chromatin conformation capture experiments and compared various methods to determine PEIs using predictive modelling of gene expression from chromatin accessibility data and predicted transcription factor (TF) motif data. We designed a novel machine learning approach that allows the prioritization of TFs binding to distal loop and promoter regions with respect to their importance for gene expression regulation. Our analysis revealed a set of core TFs that are part of enhancer-promoter loops involving YY1 in different cell lines.

CONCLUSION

We present a novel approach that can be used to prioritize TFs involved in distal and promoter-proximal regulatory events by integrating chromatin accessibility, conformation, and gene expression data. We show that the integration of chromatin conformation data can improve gene expression prediction and aids model interpretability.

CTCF mediates chromatin looping via N-terminal domain-dependent cohesin retention

The DNA-binding protein CCCTC-binding factor (CTCF) and the cohesin complex function together to shape chromatin architecture in mammalian cells, but the molecular details of this process remain unclear. Here, we demonstrate that a 79-aa region within the CTCF N terminus is essential for cohesin positioning at CTCF binding sites and chromatin loop formation. However, the N terminus of CTCF fused to artificial zinc fingers was not sufficient to redirect cohesin to non-CTCF binding sites, indicating a lack of an autonomously functioning domain in CTCF responsible for cohesin positioning. BORIS (CTCFL), a germline-specific paralog of CTCF, was unable to anchor cohesin to CTCF DNA binding sites. Furthermore, CTCF-BORIS chimeric constructs provided evidence that, besides the N terminus of CTCF, the first two CTCF zinc fingers, and likely the 3D geometry of CTCF-DNA complexes, are also involved in cohesin retention. Based on this knowledge, we were able to convert BORIS into CTCF with respect to cohesin positioning, thus providing additional molecular details of the ability of CTCF to retain cohesin. Taken together, our data provide insight into the process by which DNA-bound CTCF constrains cohesin movement to shape spatiotemporal genome organization.

A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods

BACKGROUND

Many genome-wide collections of candidate cis-regulatory elements (cCREs) have been defined using genomic and epigenomic data, but it remains a major challenge to connect these elements to their target genes.

RESULTS

To facilitate the development of computational methods for predicting target genes, we develop a Benchmark of candidate Enhancer-Gene Interactions (BENGI) by integrating the recently developed Registry of cCREs with experimentally derived genomic interactions. We use BENGI to test several published computational methods for linking enhancers with genes, including signal correlation and the TargetFinder and PEP supervised learning methods. We find that while TargetFinder is the best-performing method, it is only modestly better than a baseline distance method for most benchmark datasets when trained and tested with the same cell type and that TargetFinder often does not outperform the distance method when applied across cell types.

CONCLUSIONS

Our results suggest that current computational methods need to be improved and that BENGI presents a useful framework for method development and testing.

Novel Approaches for Identifying the Molecular Background of Schizophrenia

Recent advances in psychiatric genetics have led to the discovery of dozens of genomic loci associated with schizophrenia. However, a gap exists between the detection of genetic associations and understanding the underlying molecular mechanisms. This review describes the basic approaches used in the so-called post-GWAS studies to generate biological interpretation of the existing population genetic data, including both molecular (creation and analysis of knockout animals, exploration of the transcriptional effects of common variants in human brain cells) and computational (fine-mapping of causal variability, gene set enrichment analysis, partitioned heritability analysis) methods. The results of the crucial studies, in which these approaches were used to uncover the molecular and neurobiological basis of the disease, are also reported.

Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes

Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes.

CERENKOV3: Clustering and molecular network-derived features improve computational prediction of functional noncoding SNPs

Identification of causal noncoding single nucleotide polymorphisms (SNPs) is important for maximizing the knowledge dividend from human genome-wide association studies (GWAS). Recently, diverse machine learning-based methods have been used for functional SNP identification; however, this task remains a fundamental challenge in computational biology. We report CERENKOV3, a machine learning pipeline that leverages clustering-derived and molecular network-derived features to improve prediction accuracy of regulatory SNPs (rSNPs) in the context of post-GWAS analysis. The clustering-derived feature, locus size (number of SNPs in the locus), derives from our locus partitioning procedure and represents the sizes of clusters based on SNP locations. We generated two molecular network-derived features from representation learning on a network representing SNP-gene and gene-gene relations. Based on empirical studies using a ground-truth SNP dataset, CERENKOV3 significantly improves rSNP recognition performance in AUPRC, AUROC, and AVGRANK (a locus-wise rank-based measure of classification accuracy we previously proposed).

Local Epigenomic Data are more Informative than Local Genome Sequence Data in Predicting Enhancer-Promoter Interactions Using Neural Networks

Enhancer-promoter interactions (EPIs) are crucial for transcriptional regulation. Mapping such interactions proves useful for understanding disease regulations and discovering risk genes in genome-wide association studies. Some previous studies showed that machine learning methods, as computational alternatives to costly experimental approaches, performed well in predicting EPIs from local sequence and/or local epigenomic data. In particular, deep learning methods were demonstrated to outperform traditional machine learning methods, and using DNA sequence data alone could perform either better than or almost as well as only utilizing epigenomic data. However, most, if not all, of these previous studies were based on randomly splitting enhancer-promoter pairs as training, tuning, and test data, which has recently been pointed out to be problematic; due to multiple and duplicating/overlapping enhancers (and promoters) in enhancer-promoter pairs in EPI data, such random splitting does not lead to independent training, tuning, and test data, thus resulting in model over-fitting and over-estimating predictive performance. Here, after correcting this design issue, we extensively studied the performance of various deep learning models with local sequence and epigenomic data around enhancer-promoter pairs. Our results confirmed much lower performance using either sequence or epigenomic data alone, or both, than reported previously. We also demonstrated that local epigenomic features were more informative than local sequence data. Our results were based on an extensive exploration of many convolutional neural network (CNN) and feed-forward neural network (FNN) structures, and of gradient boosting as a representative of traditional machine learning.

In silico prediction of high-resolution Hi-C interaction matrices

The three-dimensional (3D) organization of the genome plays an important role in gene regulation bringing distal sequence elements in 3D proximity to genes hundreds of kilobases away. Hi-C is a powerful genome-wide technique to study 3D genome organization. Owing to experimental costs, high resolution Hi-C datasets are limited to a few cell lines. Computational prediction of Hi-C counts can offer a scalable and inexpensive approach to examine 3D genome organization across multiple cellular contexts. Here we present HiC-Reg, an approach to predict contact counts from one-dimensional regulatory signals. HiC-Reg predictions identify topologically associating domains and significant interactions that are enriched for CCCTC-binding factor (CTCF) bidirectional motifs and interactions identified from complementary sources. CTCF and chromatin marks, especially repressive and elongation marks, are most important for HiC-Reg’s predictive performance. Taken together, HiC-Reg provides a powerful framework to generate high-resolution profiles of contact counts that can be used to study individual locus level interactions and higher-order organizational units of the genome.

Existing computational approaches to predict long-range regulatory interactions do not fully exploit high-resolution Hi-C datasets. Here the authors present a Random Forests regression-based approach to predict high-resolution Hi-C counts using one-dimensional regulatory genomic signals.

Integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization

Despite large experimental and computational efforts aiming to dissect the mechanisms underlying disease risk, mapping cis-regulatory elements to target genes remains a challenge. Here, we introduce a matrix factorization framework to integrate physical and functional interaction data of genomic segments. The framework was used to predict a regulatory network of chromatin interaction edges linking more than 20 000 promoters and 1.8 million enhancers across 127 human reference epigenomes, including edges that are present in any of the input datasets. Our network integrates functional evidence of correlated activity patterns from epigenomic data and physical evidence of chromatin interactions. An important contribution of this work is the representation of heterogeneous data with different qualities as networks. We show that the unbiased integration of independent data sources suggestive of regulatory interactions produces meaningful associations supported by existing functional and physical evidence, correlating with expected independent biological features.

Identification of gene specific cis-regulatory elements during differentiation of mouse embryonic stem cells: An integrative approach using high-throughput datasets

Gene expression governs cell fate, and is regulated via a complex interplay of transcription factors and molecules that change chromatin structure. Advances in sequencing-based assays have enabled investigation of these processes genome-wide, leading to large datasets that combine information on the dynamics of gene expression, transcription factor binding and chromatin structure as cells differentiate. While numerous studies focus on the effects of these features on broader gene regulation, less work has been done on the mechanisms of gene-specific transcriptional control. In this study, we have focussed on the latter by integrating gene expression data for the in vitro differentiation of murine ES cells to macrophages and cardiomyocytes, with dynamic data on chromatin structure, epigenetics and transcription factor binding. Combining a novel strategy to identify communities of related control elements with a penalized regression approach, we developed individual models to identify the potential control elements predictive of the expression of each gene. Our models were compared to an existing method and evaluated using the existing literature and new experimental data from embryonic stem cell differentiation reporter assays. Our method is able to identify transcriptional control elements in a gene specific manner that reflect known regulatory relationships and to generate useful hypotheses for further testing.

Chromatin three-dimensional interactions mediate genetic effects on gene expression

Studying the genetic basis of gene expression and chromatin organization is key to characterizing the effect of genetic variability on the function and structure of the human genome. Here we unravel how genetic variation perturbs gene regulation using a dataset combining activity of regulatory elements, gene expression, and genetic variants across 317 individuals and two cell types. We show that variability in regulatory activity is structured at the intra- and interchromosomal levels within 12,583 cis-regulatory domains and 30 trans-regulatory hubs that highly reflect the local (that is, topologically associating domains) and global (that is, open and closed chromatin compartments) nuclear chromatin organization. These structures delimit cell type-specific regulatory networks that control gene expression and coexpression and mediate the genetic effects of cis- and trans-acting regulatory variants on genes.

EAGLE: An algorithm that utilizes a small number of genomic features to predict tissue/cell type-specific enhancer-gene interactions

Long-range regulation by distal enhancers is crucial for many biological processes. The existing methods for enhancer-target gene prediction often require many genomic features. This makes them difficult to be applied to many cell types, in which the relevant datasets are not always available. Here, we design a tool EAGLE, an enhancer and gene learning ensemble method for identification of Enhancer-Gene (EG) interactions. Unlike existing tools, EAGLE used only six features derived from the genomic features of enhancers and gene expression datasets. Cross-validation revealed that EAGLE outperformed other existing methods. Enrichment analyses on special transcriptional factors, epigenetic modifications, and eQTLs demonstrated that EAGLE could distinguish the interacting pairs from non- interacting ones. Finally, EAGLE was applied to mouse and human genomes and identified 7,680,203 and 7,437,255 EG interactions involving 31,375 and 43,724 genes, 138,547 and 177,062 enhancers across 89 and 110 tissue/cell types in mouse and human, respectively. The obtained interactions are accessible through an interactive database enhanceratlas.org. The EAGLE method is available at https://github.com/EvansGao/EAGLE and the predicted datasets are available in http://www.enhanceratlas.org/.

Enhancers are DNA sequences that interact with promoters and activate target genes. Since enhancers often located far from the target genes and the nearest genes are not always the targets of the enhancers, the prediction of enhancer-target gene relationships is a big challenge. Although a few computational tools are designed for the prediction of enhancer-target genes, it’s difficult to apply them in most tissue/cell types due to a lack of enough genomic datasets. Here we proposed a new method, EAGLE, which utilizes a small number of genomic features to predict tissue/cell type-specific enhancer-gene interactions. Comparing with other existing tools, EAGLE displayed a better performance in the 10-fold cross-validation and cross-sample test. Moreover, the predictions by EAGLE were validated by other independent evidence such as the enrichment of relevant transcriptional factors, epigenetic modifications, and eQTLs. Finally, we integrated the enhancer-target relationships obtained from human and mouse genomes into an interactive database EnhancerAtlas, http://www.enhanceratlas.org/.

Enhancer LncRNAs Influence Chromatin Interactions in Different Ways

More than 98% of the human genome does not encode proteins, and the vast majority of the noncoding regions have not been well studied. Some of these regions contain enhancers and functional non-coding RNAs. Previous research suggested that enhancer transcripts could be potent independent indicators of enhancer activity, and some enhancer lncRNAs (elncRNAs) have been proven to play critical roles in gene regulation. Here, we identified enhancer–promoter interactions from high-throughput chromosome conformation capture (Hi-C) data. We found that elncRNAs were highly enriched surrounding chromatin loop anchors. Additionally, the interaction frequency of elncRNA-associated enhancer–promoter pairs was significantly higher than the interaction frequency of other enhancer–promoter pairs, suggesting that elncRNAs may reinforce the interactions between enhancers and promoters. We also found that elncRNA expression levels were positively correlated with the interaction frequency of enhancer–promoter pairs. The promoters interacting with elncRNA-associated enhancers were rich in RNA polymerase II and YY1 transcription factor binding sites. We clustered enhancer–promoter pairs into different groups to reflect the different ways in which elncRNAs could influence enhancer–promoter pairs. Interestingly, G-quadruplexes were found to potentially mediate some enhancer–promoter interaction pairs, and the interaction frequency of these pairs was significantly higher than that of other enhancer–promoter pairs. We also found that the G-quadruplexes on enhancers were highly related to the expression of elncRNAs. G-quadruplexes located in the promoters of elncRNAs led to high expression of elncRNAs, whereas G-quadruplexes located in the gene bodies of elncRNAs generally resulted in low expression of elncRNAs.

Regulatory interaction between the ZPBP2-ORMDL3/Zpbp2-Ormdl3 region and the circadian clock

Genome-wide association study (GWAS) loci for several immunity-mediated diseases (early onset asthma, inflammatory bowel disease (IBD), primary biliary cholangitis, and rheumatoid arthritis) map to chromosomal region 17q12-q21. The predominant view is that association between 17q12-q21 alleles and increased risk of developing asthma or IBD is due to regulatory variants. ORM sphingolipid biosynthesis regulator (ORMDL3) residing in this region is the most promising gene candidate for explaining association with disease. However, the relationship between 17q12-q21 alleles and disease is complex suggesting contributions from other factors, such as trans-acting genetic and environmental modifiers or circadian rhythms. Circadian rhythms regulate expression levels of thousands of genes and their dysregulation is implicated in the etiology of several common chronic inflammatory diseases. However, their role in the regulation of the 17q12-q21 genes has not been investigated. Moreover, the core clock gene nuclear receptor subfamily 1, group D, member 1 (NR1D1) resides about 200 kb distal to the GWAS region. We hypothesized that circadian rhythms influenced gene expression levels in 17q12-q21 region and conversely, regulatory elements in this region influenced transcription of the core clock gene NR1D1 in cis. To test these hypotheses, we examined the diurnal expression profiles of zona pellucida binding protein 2 (ZPBP2/Zpbp2), gasdermin B (GSDMB), and ORMDL3/Ormdl3 in human and mouse tissues and analyzed the impact of genetic variation in the ZPBP2/Zpbp2 region on NR1D1/Nr1d1 expression. We found that Ormdl3 and Zpbp2 were controlled by the circadian clock in a tissue-specific fashion. We also report that deletion of the Zpbp2 region altered the expression profile of Nr1d1 in lungs and ileum in a time-dependent manner. In liver, the deletion was associated with enhanced expression of Ormdl3. We provide the first evidence that disease-associated genes Zpbp2 and Ormdl3 are regulated by circadian rhythms and the Zpbp2 region influences expression of the core clock gene Nr1d1.

GATA3-Controlled Nucleosome Eviction Drives MYC Enhancer Activity in T-cell Development and Leukemia

Long-range enhancers govern the temporal and spatial control of gene expression; however, the mechanisms that regulate enhancer activity during normal and malignant development remain poorly understood. Here, we demonstrate a role for aberrant chromatin accessibility in the regulation of MYC expression in T-cell lymphoblastic leukemia (T-ALL). Central to this process, the NOTCH1-MYC enhancer (N-Me), a long-range T cell-specific MYC enhancer, shows dynamic changes in chromatin accessibility during T-cell specification and maturation and an aberrant high degree of chromatin accessibility in mouse and human T-ALL cells. Mechanistically, we demonstrate that GATA3-driven nucleosome eviction dynamically modulates N-Me enhancer activity and is strictly required for NOTCH1-induced T-ALL initiation and maintenance. These results directly implicate aberrant regulation of chromatin accessibility at oncogenic enhancers as a mechanism of leukemic transformation. SIGNIFICANCE: MYC is a major effector of NOTCH1 oncogenic programs in T-ALL. Here, we show a major role for GATA3-mediated enhancer nucleosome eviction as a driver of MYC expression and leukemic transformation. These results support the role of aberrant chromatin accessibility and consequent oncogenic MYC enhancer activation in NOTCH1-induced T-ALL.This article is highlighted in the In This Issue feature, p. 1631.

Integrative characterization of G-Quadruplexes in the three-dimensional chromatin structure

DNA molecules are highly compacted in the eukaryotic nucleus where distal regulatory elements reach their targets through three-dimensional chromosomal interactions. G-quadruplexes, stable four-stranded non-canonical DNA structures, can change local chromatin organization through the exclusion of nucleosomes. However, the relationship between G-quadruplexes and higher-order genome organization remains unknown. Here, we found that G-quadruplexes are significantly enriched at boundaries of topological associated domains (TADs). Architectural protein occupancy, which plays critical roles in the formation of TADs, was highly correlated with the content of G-quadruplexes at TAD boundaries. Moreover, adjacent boundaries containing G-quadruplexes frequently interacted with each other because of the high enrichment of architectural protein binding sites. Similar to CCCTC-binding factor (CTCF) binding sites, G-quadruplexes also showed strong insulation ability in the separation of adjacent regions. Additionally, the insulation ability of CTCF binding sites and TAD boundaries was significantly reinforced by G-quadruplexes. Furthermore, G-quadruplex motifs on different strands were associated with the orientation of CTCF binding sites. These findings suggest a potential role for G-quadruplexes in loop extrusion. The enrichment of transcription factor binding sites (TFBSs) around regulatory elements containing G-quadruplexes led to frequent interactions between regulatory elements containing G-quadruplexes. Intriguingly, more than 99% of G-quadruplexes overlapped with TFBSs. The binding sites of CTCF and cohesin proteins were preferentially located surrounding G-quadruplexes. Accordingly, we proposed a new mechanism of long-distance gene regulation in which G-quadruplexes are involved in distal interactions between enhancers and promoters.

Rich Chromatin Structure Prediction from Hi-C Data

Recent studies involving the 3-dimensional conformation of chromatin have revealed the important role it has to play in different processes within the cell. These studies have also led to the discovery of densely interacting segments of the chromosome, called topologically associating domains. The accurate identification of these domains from Hi-C interaction data is an interesting and important computational problem for which numerous methods have been proposed. Unfortunately, most existing algorithms designed to identify these domains assume that they are non-overlapping whereas there is substantial evidence to believe a nested structure exists. We present a methodology to predict hierarchical chromatin domains using chromatin conformation capture data. Our method predicts domains at different resolutions, calculated using intrinsic properties of the chromatin data, and effectively clusters these to construct the hierarchy. At each individual level, the domains are non-overlapping in such a way that the intra-domain interaction frequencies are maximized. We show that our predicted structure is highly enriched for actively transcribing housekeeping genes and various chromatin markers, including CTCF, around the domain boundaries. We also show that large-scale domains, at multiple resolutions within our hierarchy, are conserved across cell types and species. We also provide comparisons against existing tools for extracting hierarchical domains. Our software, Matryoshka, is written in C++11 and licensed under GPL v3; it is available at https://github.com/COMBINE-lab/matryoshka.

DiseaseEnhancer: a resource of human disease-associated enhancer catalog

Large-scale sequencing studies discovered substantial genetic variants occurring in enhancers which regulate genes via long range chromatin interactions. Importantly, such variants could affect enhancer regulation by changing transcription factor bindings or enhancer hijacking, and in turn, make an essential contribution to disease progression. To facilitate better usage of published data and exploring enhancer deregulation in various human diseases, we created DiseaseEnhancer (http://biocc.hrbmu.edu.cn/DiseaseEnhancer/), a manually curated database for disease-associated enhancers. As of July 2017, DiseaseEnhancer includes 847 disease-associated enhancers in 143 human diseases. Database features include basic enhancer information (i.e. genomic location and target genes); disease types; associated variants on the enhancer and their mediated phenotypes (i.e. gain/loss of enhancer and the alterations of transcription factor bindings). We also include a feature on our website to export any query results into a file and download the full database. DiseaseEnhancer provides a promising avenue for researchers to facilitate the understanding of enhancer deregulation in disease pathogenesis, and identify new biomarkers for disease diagnosis and therapy.

Using What We Already Have: Uncovering New Drug Repurposing Strategies in Existing Omics Data

The promise of drug repurposing is to accelerate the translation of knowledge to treatment of human disease, bypassing common challenges associated with drug development to be more time- and cost-efficient. Repurposing has an increased chance of success due to the previous validation of drug safety and allows for the incorporation of omics. Hypothesis-generating omics processes inform drug repurposing decision-making methods on drug efficacy and toxicity. This review summarizes drug repurposing strategies and methodologies in the context of the following omics fields: genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, phenomics, pregomics, and personomics. While each omics field has specific strengths and limitations, incorporating omics into the drug repurposing landscape is integral to its success.

Computational Biology Solutions to Identify Enhancers-target Gene Pairs

Enhancers are non-coding regulatory elements that are distant from their target gene. Their characterization still remains elusive especially due to challenges in achieving a comprehensive pairing of enhancers and target genes. A number of computational biology solutions have been proposed to address this problem leveraging the increasing availability of functional genomics data and the improved mechanistic understanding of enhancer action. In this review we focus on computational methods for genome-wide definition of enhancer-target gene pairs. We outline the different classes of methods, as well as their main advantages and limitations. The types of information integrated by each method, along with details on their applicability are presented and discussed. We especially highlight the technical challenges that are still unresolved and hamper the effective achievement of a satisfactory and comprehensive solution. We expect this field will keep evolving in the coming years due to the ever-growing availability of data and increasing insights into enhancers crucial role in regulating genome functionality.

Predicting three-dimensional genome organization with chromatin states

We introduce a computational model to simulate chromatin structure and dynamics. Starting from one-dimensional genomics and epigenomics data that are available for hundreds of cell types, this model enables de novo prediction of chromatin structures at five-kilo-base resolution. Simulated chromatin structures recapitulate known features of genome organization, including the formation of chromatin loops, topologically associating domains (TADs) and compartments, and are in quantitative agreement with chromosome conformation capture experiments and super-resolution microscopy measurements. Detailed characterization of the predicted structural ensemble reveals the dynamical flexibility of chromatin loops and the presence of cross-talk among neighboring TADs. Analysis of the model's energy function uncovers distinct mechanisms for chromatin folding at various length scales and suggests a need to go beyond simple A/B compartment types to predict specific contacts between regulatory elements using polymer simulations.

Chromatin interactions reveal novel gene targets for drug repositioning in rheumatic diseases

Objectives

There is a need to identify effective treatments for rheumatic diseases, and while genetic studies have been successful it is unclear which genes contribute to the disease. Using our existing Capture Hi-C data on three rheumatic diseases, we can identify potential causal genes which are targets for existing drugs and could be repositioned for use in rheumatic diseases.

Methods

High confidence candidate causal genes were identified using Capture Hi-C data from B cells and T cells. These genes were used to interrogate drug target information from DrugBank to identify existing treatments, which could be repositioned to treat these diseases. The approach was refined using Ingenuity Pathway Analysis to identify enriched pathways and therefore further treatments relevant to the disease.

Results

Overall, 454 high confidence genes were identified. Of these, 48 were drug targets (108 drugs) and 11 were existing therapies used in the treatment of rheumatic diseases. After pathway analysis refinement, 50 genes remained, 13 of which were drug targets (33 drugs). However considering targets across all enriched pathways, a further 367 drugs were identified for potential repositioning.

Conclusion

Capture Hi-C has the potential to identify therapies which could be repositioned to treat rheumatic diseases. This was particularly successful for rheumatoid arthritis, where six effective, biologic treatments were identified. This approach may therefore yield new ways to treat patients, enhancing their quality of life and reducing the economic impact on healthcare providers. As additional cell types and other epigenomic data sets are generated, this prospect will improve further.

Perturbing Enhancer Activity in Cancer Therapy

Tight regulation of gene transcription is essential for normal development, tissue homeostasis, and disease-free survival. Enhancers are distal regulatory elements in the genome that provide specificity to gene expression programs and are frequently misregulated in cancer. Recent studies examined various enhancer-driven malignant dependencies and identified different approaches to specifically target these programs. In this review, we describe numerous features that make enhancers good transcriptional targets in cancer therapy and discuss different approaches to overcome enhancer perturbation. Interestingly, a number of approved therapeutic agents, such as cyclosporine, steroid hormones, and thiazolidinediones, actually function by affecting enhancer landscapes by directly targeting very specific transcription factor programs. More recently, a broader approach to targeting deregulated enhancer programs has been achieved via Bromodomain and Extraterminal (BET) inhibition or perturbation of transcription-related cyclin-dependent kinases (CDK). One challenge to enhancer-targeted therapy is proper patient stratification. We suggest that monitoring of enhancer RNA (eRNA) expression may serve as a unique biomarker of enhancer activity that can help to predict and monitor responsiveness to enhancer-targeted therapies. A more thorough investigation of cancer-specific enhancers and the underlying mechanisms of deregulation will pave the road for an effective utilization of enhancer modulators in a precision oncology approach to cancer treatment.

Joint analyses of multi-tissue Hi-C and eQTL data demonstrate close spatial proximity between eQTLs and their target genes

Background

Gene regulation is important for cells and tissues to function. It has been studied from two aspects at the genomic level, the identification of expression quantitative trait loci (eQTLs) and identification of long-range chromatin interactions. It is important to understand their relationship, such as whether eQTLs regulate their target genes through physical chromatin interaction. Although chromatin interactions have been widely believed to be one of the main mechanisms underlying eQTLs, most evidence came from studies of cell lines and yet no direct evidence exists for tissues.

Results

We performed various joint analyses of eQTL and high-throughput chromatin conformation capture (Hi-C) data from 11 human primary tissue types and 2 human cell lines. We found that chromatin interaction frequency is positively associated with the number of genes that have eQTLs and that eQTLs and their target genes tend to fall into the same topologically associating domain (TAD). These results are consistent across all tissues and cell lines we evaluated. Moreover, in 6 out of 11 tissues (aorta, dorsolateral prefrontal cortex, hippocampus, pancreas, small bowel, and spleen), tissue-specific eQTLs are significantly enriched in tissue-specific frequently interacting regions (FIREs).

Conclusions

Our data have demonstrated the close spatial proximity between eQTLs and their target genes among multiple human primary tissues.

Electronic supplementary material

The online version of this article (10.1186/s12863-019-0744-x) contains supplementary material, which is available to authorized users.

Applications of ENCODE data to Systematic Analyses via Data Integration

Large-scale genomic data have been utilized to generate unprecedented biological findings and new hypotheses. To delineate functional elements in the human genome, the Encyclopedia of DNA Elements (ENCODE) project has generated an enormous amount of genomic data, yielding around 7,000 data profiles in different cell and tissue types. In this article, we reviewed the systematic analyses that have integrated ENCODE data with other data sources to reveal new biological insights, ranging from human genome annotation to the identification of new candidate drugs. These analyses demonstrate the critical impact of ENCODE data on basic biology and translational research.

Genome-wide association and transcriptome studies identify target genes and risk loci for breast cancer

Genome-wide association studies (GWAS) have identified more than 170 breast cancer susceptibility loci. Here we hypothesize that some risk-associated variants might act in non-breast tissues, specifically adipose tissue and immune cells from blood and spleen. Using expression quantitative trait loci (eQTL) reported in these tissues, we identify 26 previously unreported, likely target genes of overall breast cancer risk variants, and 17 for estrogen receptor (ER)-negative breast cancer, several with a known immune function. We determine the directional effect of gene expression on disease risk measured based on single and multiple eQTL. In addition, using a gene-based test of association that considers eQTL from multiple tissues, we identify seven (and four) regions with variants associated with overall (and ER-negative) breast cancer risk, which were not reported in previous GWAS. Further investigation of the function of the implicated genes in breast and immune cells may provide insights into the etiology of breast cancer.

Risk variants disrupting enhancers of TH1 and TREG cells in type 1 diabetes

Functional interpretation of noncoding genetic variants identified by genome-wide association studies is a major challenge in human genetics and gene regulation. We generated epigenomics data using primary cells from type 1 diabetes patients. Using these data, we identified and validated multiple novel risk variants for this disease. In addition, our ranked list of candidate risk SNPs represents the most comprehensive annotation based on T1D-specific T-cell data. Because many autoimmune diseases share some genetic underpinnings, our dataset may be used to understand causal noncoding mutations in related autoimmune diseases.

Genome-wide association studies (GWASs) have revealed 59 genomic loci associated with type 1 diabetes (T1D). Functional interpretation of the SNPs located in the noncoding region of these loci remains challenging. We perform epigenomic profiling of two enhancer marks, H3K4me1 and H3K27ac, using primary T_H1 and T_REG cells isolated from healthy and T1D subjects. We uncover a large number of deregulated enhancers and altered transcriptional circuitries in both cell types of T1D patients. We identify four SNPs (rs10772119, rs10772120, rs3176792, rs883868) in linkage disequilibrium (LD) with T1D-associated GWAS lead SNPs that alter enhancer activity and expression of immune genes. Among them, rs10772119 and rs883868 disrupt the binding of retinoic acid receptor α (RARA) and Yin and Yang 1 (YY1), respectively. Loss of binding by YY1 also results in the loss of long-range enhancer–promoter interaction. These findings provide insights into how noncoding variants affect the transcriptomes of two T-cell subtypes that play critical roles in T1D pathogenesis.

Shaping the nebulous enhancer in the era of high-throughput assays and genome editing

Since the 1st discovery of transcriptional enhancers in 1981, their textbook definition has remained largely unchanged in the past 37 years. With the emergence of high-throughput assays and genome editing, which are switching the paradigm from bottom-up discovery and testing of individual enhancers to top-down profiling of enhancer activities genome-wide, it has become increasingly evidenced that this classical definition has left substantial gray areas in different aspects. Here we survey a representative set of recent research articles and report the definitions of enhancers they have adopted. The results reveal that a wide spectrum of definitions is used usually without the definition stated explicitly, which could lead to difficulties in data interpretation and downstream analyses. Based on these findings, we discuss the practical implications and suggestions for future studies.

Functional Analysis of Promoter Variants in Genes Involved in Sex Steroid Action, DNA Repair and Cell Cycle Control

Genetic variants affecting the regulation of gene expression are among the main causes of human diversity. The potential importance of regulatory polymorphisms is underscored by results from Genome Wide Association Studies, which have already implicated such polymorphisms in the susceptibility to complex diseases such as breast cancer. In this study, we re-sequenced the promoter regions of 24 genes involved in pathways related to breast cancer including sex steroid action, DNA repair, and cell cycle control in 60 unrelated Caucasian individuals. We constructed haplotypes and assessed the functional impact of promoter variants using gene reporter assays and electrophoretic mobility shift assays. We identified putative functional variants within the promoter regions of estrogen receptor 1 (ESR1), ESR2, forkhead box A1 (FOXA1), ubiquitin interaction motif containing 1 (UIMC1) and cell division cycle 7 (CDC7). The functional polymorphism on CDC7, rs13447455, influences CDC7 transcriptional activity in an allele-specific manner and alters DNA–protein complex formation in breast cancer cell lines. Moreover, results from the Breast Cancer Association Consortium show a marginal association between rs13447455 and breast cancer risk (p = 9.3 × 10⁻⁵), thus warranting further investigation. Furthermore, our study has helped provide methodological solutions to some technical difficulties that were encountered with gene reporter assays, particularly regarding inter-clone variability and statistical consistency.

New guidelines for DNA methylome studies regarding 5-hydroxymethylcytosine for understanding transcriptional regulation

Many DNA methylome profiling methods cannot distinguish between 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC). Because 5mC typically acts as a repressive mark whereas 5hmC is an intermediate form during active demethylation, the inability to separate their signals could lead to incorrect interpretation of the data. Is the extra information contained in 5hmC signals worth the additional experimental and computational costs? Here we combine whole-genome bisulfite sequencing (WGBS) and oxidative WGBS (oxWGBS) data in various human tissues to investigate the quantitative relationships between gene expression and the two forms of DNA methylation at promoters, transcript bodies, and immediate downstream regions. We find that 5mC and 5hmC signals correlate with gene expression in the same direction in most samples. Considering both types of signals increases the accuracy of expression levels inferred from methylation data by a median of 18.2% as compared to having only WGBS data, showing that the two forms of methylation provide complementary information about gene expression. Differential analysis between matched tumor and normal pairs is particularly affected by the superposition of 5mC and 5hmC signals in WGBS data, with at least 25%–40% of the differentially methylated regions (DMRs) identified from 5mC signals not detected from WGBS data. Our results also confirm a previous finding that methylation signals at transcript bodies are more indicative of gene expression levels than promoter methylation signals. Overall, our study provides data for evaluating the cost-effectiveness of some experimental and analysis options in the study of DNA methylation in normal and cancer samples.

Cell Specificity of Human Regulatory Annotations and Their Genetic Effects on Gene Expression

Epigenomic signatures from histone marks and transcription factor (TF)-binding sites have been used to annotate putative gene regulatory regions. However, a direct comparison of these diverse annotations is missing, and it is unclear how genetic variation within these annotations affects gene expression. Here, we compare five widely used annotations of active regulatory elements that represent high densities of one or more relevant epigenomic marks-"super" and "typical" (nonsuper) enhancers, stretch enhancers, high-occupancy target (HOT) regions, and broad domains-across the four matched human cell types for which they are available. We observe that stretch and super enhancers cover cell type-specific enhancer "chromatin states," whereas HOT regions and broad domains comprise more ubiquitous promoter states. Expression quantitative trait loci (eQTL) in stretch enhancers have significantly smaller effect sizes compared to those in HOT regions. Strikingly, chromatin accessibility QTL in stretch enhancers have significantly larger effect sizes compared to those in HOT regions. These observations suggest that stretch enhancers could harbor genetically primed chromatin to enable changes in TF binding, possibly to drive cell type-specific responses to environmental stimuli. Our results suggest that current eQTL studies are relatively underpowered or could lack the appropriate environmental context to detect genetic effects in the most cell type-specific "regulatory annotations," which likely contributes to infrequent colocalization of eQTL with genome-wide association study signals.

Functional SNPs in the Human Autoimmunity-Associated Locus 17q12-21

Genome-wide association studies (GWASes) revealed several single-nucleotide polymorphisms (SNPs) in the human 17q12-21 locus associated with autoimmune diseases. However, follow-up studies are still needed to identify causative SNPs directly mediating autoimmune risk in the locus. We have chosen six SNPs in high linkage disequilibrium with the GWAS hits that showed the strongest evidence of causality according to association pattern and epigenetic data and assessed their functionality in a local genomic context using luciferase reporter system. We found that rs12946510, rs4795397, rs12709365, and rs8067378 influenced the reporter expression level in leukocytic cell lines. The strongest effect visible in three distinct cell types was observed for rs12946510 that is predicted to alter MEF2A/C and FOXO1 binding sites.