Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Heritability and polygenic predictionIn the EUR sample, the SNP-based heritability (h 2 SNP ) (that is, the proportion of variance in liability attributable to all measured SNPs)

Recent research on disparate psychiatric disorders has implicated rare variants in genes involved in global gene regulation and chromatin modification, as well as many common variants located primarily in regulatory regions of the genome. Understanding precisely how these variants contribute to disease will require a deeper appreciation for the mechanisms of gene regulation in the developing and adult human brain. The PsychENCODE project aims to produce a public resource of multidimensional genomic data using tissue- and cell type–specific samples from approximately 1,000 phenotypically well-characterized, high-quality healthy and disease-affected human post-mortem brains, as well as functionally characterize disease-associated regulatory elements and variants in model systems. We are beginning with a focus on autism spectrum disorder, bipolar disorder and schizophrenia, and expect that this knowledge will apply to a wide variety of psychiatric disorders. This paper outlines the motivation and design of PsychENCODE.

Recent studies indicate that DNA methylation can be used to identify transcriptional enhancers, but no systematic approach has been developed for genome-wide identification and analysis of enhancers based on DNA methylation. We describe ELMER (Enhancer Linking by Methylation/Expression Relationships), an R-based tool that uses DNA methylation to identify enhancers and correlates enhancer state with expression of nearby genes to identify transcriptional targets. Transcription factor motif analysis of enhancers is coupled with expression analysis of transcription factors to infer upstream regulators. Using ELMER, we investigated more than 2,000 tumor samples from The Cancer Genome Atlas. We identified networks regulated by known cancer drivers such as GATA3 and FOXA1 (breast cancer), SOX17 and FOXA2 (endometrial cancer), and NFE2L2, SOX2, and TP63 (squamous cell lung cancer). We also identified novel networks with prognostic associations, including RUNX1 in kidney cancer. We propose ELMER as a powerful new paradigm for understanding the cis-regulatory interface between cancer-associated transcription factors and their functional target genes.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0668-3) contains supplementary material, which is available to authorized users.

BackgroundThe TCF7L2 transcription factor is linked to a variety of human diseases, including type 2 diabetes and cancer. One mechanism by which TCF7L2 could influence expression of genes involved in diverse diseases is by binding to distinct regulatory regions in different tissues. To test this hypothesis, we performed ChIP-seq for TCF7L2 in six human cell lines.ResultsWe identified 116,000 non-redundant TCF7L2 binding sites, with only 1,864 sites common to the six cell lines. Using ChIP-seq, we showed that many genomic regions that are marked by both H3K4me1 and H3K27Ac are also bound by TCF7L2, suggesting that TCF7L2 plays a critical role in enhancer activity. Bioinformatic analysis of the cell type-specific TCF7L2 binding sites revealed enrichment for multiple transcription factors, including HNF4alpha and FOXA2 motifs in HepG2 cells and the GATA3 motif in MCF7 cells. ChIP-seq analysis revealed that TCF7L2 co-localizes with HNF4alpha and FOXA2 in HepG2 cells and with GATA3 in MCF7 cells. Interestingly, in MCF7 cells the TCF7L2 motif is enriched in most TCF7L2 sites but is not enriched in the sites bound by both GATA3 and TCF7L2. This analysis suggested that GATA3 might tether TCF7L2 to the genome at these sites. To test this hypothesis, we depleted GATA3 in MCF7 cells and showed that TCF7L2 binding was lost at a subset of sites. RNA-seq analysis suggested that TCF7L2 represses transcription when tethered to the genome via GATA3.ConclusionsOur studies demonstrate a novel relationship between GATA3 and TCF7L2, and reveal important insights into TCF7L2-mediated gene regulation.

BackgroundGene expression is epigenetically regulated by a combination of histone modifications and methylation of CpG dinucleotides in promoters. In normal cells, CpG-rich promoters are typically unmethylated, marked with histone modifications such as H3K4me3, and are highly active. During neoplastic transformation, CpG dinucleotides of CG-rich promoters become aberrantly methylated, corresponding with the removal of active histone modifications and transcriptional silencing. Outside of promoter regions, distal enhancers play a major role in the cell type-specific regulation of gene expression. Enhancers, which function by bringing activating complexes to promoters through chromosomal looping, are also modulated by a combination of DNA methylation and histone modifications.ResultsHere we use HCT116 colorectal cancer cells with and without mutations in DNA methyltransferases, the latter of which results in a 95% reduction in global DNA methylation levels. These cells are used to study the relationship between DNA methylation, histone modifications, and gene expression. We find that the loss of DNA methylation is not sufficient to reactivate most of the silenced promoters. In contrast, the removal of DNA methylation results in the activation of a large number of enhancer regions as determined by the acquisition of active histone marks.ConclusionsAlthough the transcriptome is largely unaffected by the loss of DNA methylation, we identify two distinct mechanisms resulting in the upregulation of distinct sets of genes. One is a direct result of DNA methylation loss at a set of promoter regions and the other is due to the presence of new intragenic enhancers.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-014-0469-0) contains supplementary material, which is available to authorized users.

Colorectal cancer (CRC) is a leading cause of cancer-related deaths in the United States. Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with increased risk for CRC. A molecular understanding of the functional consequences of this genetic variation has been complicated because each GWAS SNP is a surrogate for hundreds of other SNPs, most of which are located in non-coding regions. Here we use genomic and epigenomic information to test the hypothesis that the GWAS SNPs and/or correlated SNPs are in elements that regulate gene expression, and identify 23 promoters and 28 enhancers. Using gene expression data from normal and tumour cells, we identify 66 putative target genes of the risk-associated enhancers (10 of which were also identified by promoter SNPs). Employing CRISPR nucleases, we delete one risk-associated enhancer and identify genes showing altered expression. We suggest that similar studies be performed to characterize all CRC risk-associated enhancers.

Motivation DNA methylation has been used to identify functional changes at transcriptional enhancers and other cis-regulatory modules (CRMs) in tumors and other disease tissues. Our R/Bioconductor package ELMER (Enhancer Linking by Methylation/Expression Relationships) provides a systematic approach that reconstructs altered gene regulatory networks (GRNs) by combining enhancer methylation and gene expression data derived from the same sample set. Results We present a completely revised version 2 of ELMER that provides numerous new features including an optional web-based interface and a new Supervised Analysis mode to use pre-defined sample groupings. We show that Supervised mode significantly increases statistical power and identifies additional GRNs and associated Master Regulators, such as SOX11 and KLF5 in Basal-like breast cancer. Availability and implementation ELMER v.2 is available as an R/Bioconductor package at http://bioconductor.org/packages/ELMER/. Supplementary information Supplementary data are available at Bioinformatics online.

Enhancers are short regulatory sequences bound by sequence-specific transcription factors and play a major role in the spatiotemporal specificity of gene expression patterns in development and disease. While it is now possible to identify enhancer regions genome-wide in both cultured cells and primary tissues using epigenomic approaches, it has been more challenging to develop methods to understand the function of individual enhancers because enhancers are located far from the gene(s) they regulate. However, it is essential to identify target genes of enhancers not only so that we can understand the role of enhancers in disease but also because this information will assist in the development of future therapeutic options. After reviewing models of enhancer function, we discuss recent methods for identifying target genes of enhancers. First, we describe chromatin structure-based approaches for directly mapping interactions between enhancers and promoters. Second, we describe the use of correlation-based approaches to link enhancer state with the activity of nearby promoters and/or gene expression. Third, we describe how to test the function of specific enhancers experimentally by perturbing enhancer-target relationships using high-throughput reporter assays and genome editing. Finally, we conclude by discussing as yet unanswered questions concerning how enhancers function, how target genes can be identified, and how to distinguish direct from indirect changes in gene expression mediated by individual enhancers.