Search

Histone modifications are critical for regulating chromatin structure and gene expression. Dysregulation of histone modifications likely contributes to disease states and cancer. Depletion of the chromatin-binding protein BRWD3, a known substrate-specificity factor of the Cul4-DDB1 E3 ubiquitin ligase complex, results in increased in H3K4me1 levels. The underlying mechanism linking BRWD3 and H3K4 methylation, however, has yet to be defined. Here, we show that depleting BRWD3 not only causes an increase in H3K4me1 levels, but also causes a decrease in H3K4me3 levels, indicating that BRWD3 influences H3K4 methylation more broadly. Using immunoprecipitation coupled to quantitative mass spectrometry, we identified an interaction between BRWD3 and the H3K4-specific demethylase 5 (KDM5/Lid), an enzyme that removes tri- and di- methyl marks from H3K4. Moreover, analysis of ChIP-seq data revealed that BRWD3 and KDM5 are significantly co- localized throughout the genome and that sites of H3K4me3 are highly enriched at BRWD3 binding sites. We show that BRWD3 promotes K48-linked polyubiquitination and degradation of KDM5 and that KDM5 degradation is dependent on both BRWD3 and Cul4. Critically, depleting KDM5 fully restores altered H3K4me3 levels and partially restores H3K4me1 levels upon BRWD3 depletion. Together, our results demonstrate that BRWD3 regulates KDM5 activity to balance H3K4 methylation levels.

Telomere healing occurs when telomerase, normally restricted to chromosome ends, acts upon a double-strand break to create a new, functional telomere. De novo telomere addition on the centromere-proximal side of a break truncates the chromosome but, by blocking resection, may allow the cell to survive an otherwise lethal event. We previously identified several sequences in the baker’s yeast,Saccharomyces cerevisiae, that act as hotspots of de novo telomere addition (termed Sites of Repair-associated Telomere Addition or SiRTAs), but the distribution and functional relevance of SiRTAs is unclear. Here, we describe a high-throughput sequencing method to measure the frequency and location of telomere addition within sequences of interest. Combining this methodology with a computational algorithm that identifies SiRTA sequence motifs, we generate the first comprehensive map of telomere-addition hotspots in yeast. Putative SiRTAs are strongly enriched in subtelomeric regions where they may facilitate formation of a new telomere following catastrophic telomere loss. In contrast, outside of subtelomeres, the distribution and orientation of SiRTAs appears random. Since truncating the chromosome at most SiRTAs would be lethal, this observation argues against selection for these sequences as sites of telomere addition per se. We find, however, that sequences predicted to function as SiRTAs are significantly more prevalent across the genome than expected by chance. Sequences identified by the algorithm bind the telomeric protein Cdc13, raising the possibility that association of Cdc13 with single-stranded regions generated during the response to DNA damage may facilitate DNA repair more generally.

Summary GSEL is a computational framework for calculating the enrichment of signatures of diverse evolutionary forces in a set of genomic regions. GSEL can flexibly integrate any sequence-based evolutionary metric and analyze sets of human genomic regions identified by genome-wide assays (e.g. GWAS, eQTL, *-seq). The core of GSEL’s approach is the generation of empirical null distributions tailored to the allele frequency and linkage disequilibrium structure of the regions of interest. We illustrate the application of GSEL to variants identified from a GWAS of body mass index, a highly polygenic trait. Availability and implementation GSEL is implemented as a fast, flexible and user-friendly python package. It is available with demonstration data at https://github.com/abraham-abin13/gsel_vec. Supplementary information Supplementary data are available at Bioinformatics online.

Objectives: To identify and summarize the top bioinformatics and translational informatics papers published in 2021 for the IMIA Yearbook. Methods: We performed a broad literature search to retrieve Bioinformatics and Translational Informatics (BTI) papers and coupled this with a series of editorial and peer reviews to identity the top papers in the area. Results: We identified a final candidate list of 15 BTI papers for peer-review; from these candidates, the top three papers were chosen to highlight in this synopsis. These papers expand the integration of multi-omics data with electronic health records and use advanced machine learning approaches to tailor models to individual patients. In addition, our honorable mention paper foreshadows the growing impact of BTI research on precision medicine through the continued development of large clinical consortia. Conclusion: In the top BTI papers this year, we observed several important trends, including the use of deep-learning approaches to analyse diverse data types, the development of integrative and web-accessible bioinformatics pipelines, and a continued focus on the power of individual genome sequencing for precision health.

Insulin-like growth factor 1 (IGF-1) influences bone turnover. Transient decreases in IGF-I levels and/or bioavailability may contribute to the detrimental effects of alcohol on bone. The goals of this non-human primate study were to i) evaluate the 20-h response of bone turnover markers to ethanol consumption and ii) assess how ethanol consumption influences the relationship between IGF-1 and these markers. Osteocalcin (bone formation), carboxyterminal cross-linking telopeptide of type 1 collagen (CTX, bone resorption), IGF-1, and IGF binding protein 1 (IGFBP-1) were measured in plasma from male rhesus macaques (N = 10, 8.4 ± 0.3 years) obtained at 12:00, 16:00, and 06:00 h during two phases: pre-ethanol (alcohol-naïve) and ethanol access. During the ethanol access phase, monkeys consumed 1.5 g/kg/day ethanol (4% w/v) beginning at 10:00 h. Osteocalcin and CTX were lower, and the ratio of osteocalcin to CTX was higher at each time point during ethanol access compared to the pre-ethanol phase. Pre-ethanol marker levels did not vary across time points, but markers varied during ethanol access. IGF-1 levels, but not IGFBP-1 levels, varied during the pre-ethanol phase. In contrast, IGF-1 levels were stable during ethanol access but IGFBP-1 levels varied. There were positive relationships between IGF-1 and turnover markers during the pre-ethanol phase, but not during ethanol access. In conclusion, chronic ethanol consumption reduces levels of bone turnover markers and blocks the normal positive relationship between IGF-1 and turnover markers and alters the normal relationship between IGF-1 and IGFBP-1. These findings support the hypothesis that chronic alcohol consumption leads to growth hormone/IGF-1 resistance.

Summary Objectives: Provide an overview of the emerging themes and notable papers which were published in 2020 in the field of Bioinformatics and Translational Informatics (BTI) for the International Medical Informatics Association Yearbook. Methods: A team of 16 individuals scanned the literature from the past year. Using a scoring rubric, papers were evaluated on their novelty, importance, and objective quality. 1,224 Medical Subject Headings (MeSH) terms extracted from these papers were used to identify themes and research focuses. The authors then used the scoring results to select notable papers and trends presented in this manuscript. Results: The search phase identified 263 potential papers and central themes of coronavirus disease 2019 (COVID-19), machine learning, and bioinformatics were examined in greater detail. Conclusions: When addressing a once in a centruy pandemic, scientists worldwide answered the call, with informaticians playing a critical role. Productivity and innovations reached new heights in both TBI and science, but significant research gaps remain.

Understanding the individuality of human brain function and dysfunction requires the integration of data on brain organization across individuals, molecular and systems scales, as well as healthy and clinical states. Here we advance this understanding by leveraging methods from computational genomics to integrate large-scale transcriptomic, neuroimaging, and electronic- health-record datasets. We inferred genetically regulated expression (gr-expression) across cortical and subcortical regions in 890 individuals from the Human Connectome Project, and validated this inference against directly assayed gene expression data from the Allen Human Brain Atlas. We discovered genes whose patterns of gr-expression associate with individual variation in neuroimaging phenotypes, and used electronic health records from the Vanderbilt Biobank to link hundreds of these genes with brain-related diseases. Finally, we showed that combined gr-expression from multiple genes improves prediction of neural phenotypes and replication with analyses of the UK Biobank. Collectively, we describe and validate a powerful new approach for linking individual variation in human gene expression, brain organization and disease risk.

BackgroundSingle-cell RNA (scRNA-seq) sequencing technologies trigger the study of individual cell gene expression and reveal the diversity within cell populations. To measure cell-to-cell similarity based on their transcription and gene expression, many dimension reduction methods are employed to retrieve corresponding low-dimensional embeddings of input scRNA-seq data to conduct clustering. However, the methods lack explainability and may not perform well with scRNA-seq data because they are not customized for high-dimensional sparse scRNA-seq data.ResultsIn this study, we propose an explainable t-SNE: cell-driven t-SNE (c-TSNE) that fuses cell differences reflected from biologically meaningful distance metrics for input data. Our study shows that the proposed method not only enhances the interpretation of the original t-SNE visualization but also demonstrates favorable single cell segregation performance on benchmark datasets compared to state-of-the-art peers. The robustness analysis shows that the proposed cell-driven t-SNE demonstrates robustness to dropout and noise in clustering. It provides a novel and practical way to investigate the interpretability of t-SNE in scRNA-seq data analysis. Unlike the general assumption that the explainability of a machine learning method may need to compromise with learning efficiency, the proposed explainable t-SNE improves both clustering efficiency and explainability. More importantly, our work suggests that widely used t-SNE can be easily misused in existing scRNA-seq analysis, because its default Euclidean distance can bring biases or meaningless results in cell difference evaluation for high-dimensional sparse scRNA-seq data. To the best of our knowledge, it is the first explainable t-SNE proposed in scRNA-seq analysis and will inspire other explainable machine learning method development in the field.ConclusionThe proposed explainable t-SNE outperforms classic t-SNE and its peers in meaningful visualization and segregation. The poor performance of the classic t-SNE highlights the importance of developing explainable machine learning methods in scRNA-seq analysis. The explainable t-SNE is a data-centric customized ML enhance efficiency in data analysis through bringing more biological insights and interpretations.

Simple Summary Epithelial–mesenchymal transition (EMT) facilitates cell migration, invasion, and, consequently, metastasis, which ultimately contributes to breast-cancer-related fatalities. In this study, we define the DNA accessibility dynamics that permit EMT and its reversal, MET. We demonstrate the progressive repression of E-cadherin, beginning with the loss of the membrane-bound fraction, and followed by the loss of CDH1 reporter expression. We identify that EMT is characterized by a global increase in accessible chromatin—nearly doubling the number of accessible regions. Furthermore, we find that regions exhibiting chromatin alterations are enriched in binding motifs for CTCF. Additionally, our data suggest that CTCF repression slows the loss of epithelial gene expression while accelerating the gain of mesenchymal gene expression, facilitating a state of partial EMT. Abstract Epithelial–mesenchymal transition (EMT) and its reversal, mesenchymal–epithelial transition (MET) drive tissue reorganization critical for early development. In carcinomas, processing through EMT, MET, or partial states promotes migration, invasion, dormancy, and metastatic colonization. As a reversible process, EMT is inherently regulated at epigenetic and epigenomic levels. To understand the epigenomic nature of reversible EMT and its partial states, we characterized chromatin accessibility dynamics, transcriptomic output, protein expression, and cellular phenotypes during stepwise reversible EMT. We find that the chromatin insulating protein machinery, including CTCF, is suppressed and re-expressed, coincident with broad alterations in chromatin accessibility, during EMT/MET, and is lower in triple-negative breast cancer cell lines with EMT features. Through an analysis of chromatin accessibility using ATAC-seq, we identify that early phases of EMT are characterized by enrichment for AP-1 family member binding motifs, but also by a diminished enrichment for CTCF binding motifs. Through a loss-of-function analysis, we demonstrate that the suppression of CTCF alters cellular plasticity, strengthening the epithelial phenotype via the upregulation of epithelial markers E-cadherin/CDH1 and downregulation of N-cadherin/CDH2. Conversely, the upregulation of CTCF leads to the upregulation of EMT gene expression and an increase in mesenchymal traits. These findings are indicative of a role of CTCF in regulating epithelial–mesenchymal plasticity and gene expression.

R-loops are involved in transcriptional regulation, DNA and histone post-translational modifications, genome replication and genome stability. To what extent R-loop abundance and genome-wide localization is actively regulated during metazoan embryogenesis is unknown. Drosophila embryogenesis provides a powerful system to address these questions due to its well-characterized developmental program, the sudden onset of zygotic transcription and available genome-wide ChIP and transcription data sets. Here, we measure the overall abundance and genome localization of R-loops in early and late-stage embryos relative to Drosophila cultured cells. We demonstrate that absolute R-loop levels change during embryogenesis and that resolution of R-loops is critical for embryonic development. R-loop mapping by strand-specific DRIP-seq reveals that R-loop localization is plastic across development, both in the genes which form R-loops and where they localize relative to gene bodies. Importantly, these changes are not driven by changes in the transcriptional program. Negative GC skew and absolute changes in AT skew are associated with R-loop formation in Drosophila. Furthermore, we demonstrate that while some chromatin binding proteins and histone modification such as H3K27me3 are associated with R-loops throughout development, other chromatin factors associated with R-loops in a developmental specific manner. Our findings highlight the importance and developmental plasticity of R-loops during Drosophila embryogenesis.

Purpose Alcohol consumption suppressed bone turnover in male non-human primates; however, it is unclear the extent to which this effect depends upon biological variables. Using archived plasma samples, we investigated whether sex, age of onset of alcohol intake, and species influence the effects of graded increases in alcohol consumption on bone turnover markers. Methods 91 male and female macaques (rhesus and cynomolgus), ranging in age from 4 years (adolescent) to 10 years (adult) were required to increase their consumption of ethanol in 30-day increments: 0 g/kg/day, followed by 0.5 g/kg/day, 1.0 g/kg/day, and, finally, 1.5 g/kg/day. Plasma osteocalcin (formation), plasma CTX (resorption) and osteocalcin to CTX ratio (turnover balance) were measured during these intervals to assess the dose-response effects of alcohol. Results We detected no relationship between dose and osteocalcin when all monkeys were combined, but there was a significant effect of sex (lower levels in females) and interactions between alcohol dose and sex (osteocalcin levels increased with dose in rhesus females). In contrast, we detected a negative linear dose-response relationship for ethanol and CTX. We did not detect a relationship between dose and osteocalcin to CTX ratio overall, but there was a significant positive relationship detected in females (no change in males). Increased age predicted lower biomarker levels for both osteocalcin and CTX. Species was a significant predictor for osteocalcin and the osteocalcin to CTX ratio in these models. Conclusion These findings indicate that age, sex, and species influence bone turnover and support the concept that factors beyond quantity of alcohol affect skeletal response to alcohol consumption., Highlights • Age, sex, and species influenced markers of bone turnover in non-human primates. • Ethanol consumption resulted in a dose-dependent reduction in CTX. • Ethanol consumption resulted in increased osteocalcin in rhesus females.

SUMMARYThe origin recognition complex (ORC) binds throughout the genome to initiate DNA replication. In metazoans, it is still unclear how ORC is targeted to specific loci to facilitate helicase loading and replication initiation. Here, we performed immunoprecipitations coupled with mass spectrometry for ORC2 in Drosophila embryos. Surprisingly, we found that ORC2 associates with multiple subunits of the Nup107-160 subcomplex of the nuclear pore. Bioinformatic analysis revealed that, relative to all modENCODE factors, nucleoporins are among the most enriched factors at ORC2 binding sites. Critically, depletion of the nucleoporin Elys, a member of the Nup107-160 complex, results in decreased ORC2 loading onto chromatin. Depleting Elys also sensitized cells to replication fork stalling, which could reflect a defect in establishing dormant replication origins. Our work reveals a new connection between ORC, replication initiation and nucleoporins, highlighting a previously unrecognized function of nucleoporins in metazoan replication initiation.

Epithelial-mesenchymal transition (EMT) and its reversal, mesenchymal-epithelial transition (MET) drive tissue reorganization critical for early development. In carcinomas, processing through EMT, MET or partial states promotes migration, invasion, dormancy, and metastatic colonization. As a reversible process, EMT is inherently regulated at epigenetic and epigenomic levels. To understand the epigenomic nature of reversible EMT and its partial states, we characterized chromatin accessibility dynamics, transcriptomic output, protein expression, and cellular phenotypes during stepwise reversible EMT. We found that the chromatin insulating protein machinery, including CTCF, is suppressed and re-expressed, coincident with broad alterations in chromatin accessibility, during EMT/MET and is lower in triple-negative breast cancer cell lines with EMT features. Through analysis of chromatin accessibility using ATAC-seq, we identify that early phases of EMT are characterized by enrichment for AP-1 family member binding motifs but also by diminished enrichment for CTCF binding motifs. Through loss-of-function analysis we demonstrate that suppression of CTCF alters cellular plasticity, facilitating entrance into a partial EMT state. These findings are indicative of a role of CTCF and chromatin reorganization for epithelial-mesenchymal plasticity.

Background: Long-term balancing selection (LTBS) can maintain allelic variation at a locus over millions of years and through speciation events. Variants shared between species, hereafter “trans-species polymorphisms” (TSPs), often result from LTBS due to host-pathogen interactions. For instance, the major histocompatibility complex (MHC) locus contains TSPs present across primates. Several hundred candidate TSPs have been identified in humans and chimpanzees; however, because many are in non-coding regions of the genome, the functions and adaptive roles for most TSPs remain unknown. Results: We integrated diverse genomic annotations, with a focus on non-coding regions, to explore the functions of 125 previously identified regions containing multiple TSPs in humans and chimpanzees. We analyzed genome-wide functional assays, expression quantitative trait loci (eQTL), genome-wide association studies (GWAS), and phenome-wide association studies (PheWAS). We identify functional annotations for 119 TSP regions, including 71 with evidence of gene regulatory function from GTEx or genome-wide functional genomics data and 21 with evidence of trait association from GWAS and PheWAS. TSPs in humans associate with many immune system phenotypes, including response to pathogens, but we also find associations with a range of other phenotypes, including body mass, alcohol intake, urate levels, chronotype, and risk-taking behavior. Conclusions: The diversity of traits associated with non-coding human TSPs further support previous hypotheses that functions beyond the immune system are subject to LTBS. Furthermore, several of these trait associations provide support and candidate genetic loci for previous hypothesis about behavioral diversity in great ape populations, such as the importance of variation in sleep cycles and risk sensitivity.

Nearly all genetic variants that influence disease risk have human-specific origins; however, the systems they influence have ancient roots that often trace back to evolutionary events long before the origin of humans. Here, we review how advances in our understanding of the genetic architectures of diseases, recent human evolution and deep evolutionary history can help explain how and why humans in modern environments become ill. Human populations exhibit differences in the prevalence of many common and rare genetic diseases. These differences are largely the result of the diverse environmental, cultural, demographic and genetic histories of modern human populations. Synthesizing our growing knowledge of evolutionary history with genetic medicine, while accounting for environmental and social factors, will help to achieve the promise of personalized genomics and realize the potential hidden in an individual’s DNA sequence to guide clinical decisions. In short, precision medicine is fundamentally evolutionary medicine, and integration of evolutionary perspectives into the clinic will support the realization of its full potential., Our evolutionary history has resulted in highly complex and sophisticated human physiology. Yet evolutionary footprints have also left us prone to diseases. In this Review, the authors discuss how events from the earliest history of life on Earth through to modern human evolution influence many disease traits and outcomes. They describe how an understanding and application of evolutionary frameworks can inform precision medicine initiatives.

Long-term balancing selection (LTBS) can maintain allelic variation at a locus over millions of years and through speciation events. Variants shared between species, hereafter “trans-species polymorphisms” (TSPs), often result from LTBS due to host-pathogen interactions. For instance, the major histocompatibility complex (MHC) locus contains TSPs present across primates. Several hundred TSPs have been identified in humans and chimpanzees; however, because many are in non-coding regions of the genome, the functions and adaptive roles for most TSPs remain unknown. We integrated diverse genomic annotations to explore the functions of 125 previously identified non-coding TSPs that are likely under LTBS since the common ancestor of humans and chimpanzees. We analyzed genome-wide functional assays, expression quantitative trait loci (eQTL), genome-wide association studies (GWAS), and phenome-wide association studies (PheWAS). We identify functional annotations for 119 TSP regions, including 71 with evidence of gene regulatory function from GTEx or genome-wide functional genomics data and 21 with evidence of trait association from GWAS and PheWAS. TSPs in humans associate with many immune system phenotypes, including response to pathogens, but we also find associations with a range of other phenotypes, including body mass, alcohol intake, urate levels, chronotype, and risk-taking behavior. The diversity of traits associated with non-coding human TSPs suggest that functions beyond the immune system are often subject to LTBS. Furthermore, several of these trait associations provide support and candidate genetic loci for previous hypothesis about behavioral diversity in great ape populations, such as the importance of variation in sleep cycles and risk sensitivity.Significance statementMost genetic variants present in human populations are young (

The hormone erythropoietin (Epo) is required for erythropoiesis, yet its molecular mechanism of action remains poorly understood, particularly with respect to chromatin dynamics. To investigate how Epo modulates the erythroid epigenome, we performed epigenetic profiling using an ex vivo murine cell system that undergoes synchronous erythroid maturation in response to Epo stimulation. Our findings define the repertoire of Epo-modulated enhancers, illuminating a new facet of Epo signaling. First, a large number of enhancers rapidly responded to Epo stimulation, revealing a cis-regulatory network of Epo-responsive enhancers. In contrast, most of the other identified enhancers remained in an active acetylated state during Epo signaling, suggesting that most erythroid enhancers are established at an earlier precursor stage. Second, we identified several hundred super-enhancers that were linked to key erythroid genes, such as Tal1, Bcl11a, and Mir144/451. Third, experimental and computational validation revealed that many predicted enhancer regions were occupied by TAL1 and enriched with DNA-binding motifs for GATA1, KLF1, TAL1/E-box, and STAT5. Additionally, many of these cis-regulatory regions were conserved evolutionarily and displayed correlated enhancer:promoter acetylation. Together, these findings define a cis-regulatory enhancer network for Epo signaling during erythropoiesis, and provide the framework for future studies involving the interplay of epigenetics and Epo signaling.

Structural variants (SVs) contribute substantially to risk of many brain related disorders including autism and schizophrenia. However, annotating the potential contribution of SVs to disease remains a major challenge. Here, we integrated high resolution SV calling from genome-sequencing in 755 human post-mortem brains with dorsal lateral prefrontal cortex RNA-sequencing from a subset of 629 samples to quantify the dosage and regulatory effects of SVs. We show that genic (p = 5.44×10−9) and regulatory SVs (enhancer p = 3.22×10−23, CTCF p = 3.86×10−18) are present at significantly lower frequencies than intergenic SVs after correcting for SV length. Copy number variants (CNVs)—deletions and duplications—exhibit a significant quantitative and directional relationship between the proportion of genic and regulatory content altered and gene expression, and the size of the effect is inversely correlated with the loss-of-function intolerance of the gene. We trained a joint linear model that leverages genic and regulatory annotations to predict expression effects of rare CNVs in independent samples (R2= 0.21-0.41). We further developed a regulatory disruption score for each CNV that aggregates the predicted expression across all affected genes weighted by the genes’ intolerance score and applied it to an independent set of SVs from 14,891 genome-sequenced individuals. Pathogenic deletions implicated in neurodevelopmental disorders by ClinGen had significantly more extreme regulatory disruption scores than the rest of the SVs. Rank ordering based on the most extreme regulatory disruption scores prioritized pathogenic deletions that would not have been prioritized by frequency or length alone. This work points to the deleteriousness of regulatory SVs, particularly those altering CTCF sites. We further provide a simple approach for functionally annotating the regulatory effects of SVs in the human brain that has potential to be useful in larger SV studies and should improve as more regulatory annotation data is generated.

Background Non-coding gene regulatory enhancers are essential to transcription in mammalian cells. As a result, a large variety of experimental and computational strategies have been developed to identify cis-regulatory enhancer sequences. Given the differences in the biological signals assayed, some variation in the enhancers identified by different methods is expected; however, the concordance of enhancers identified by different methods has not been comprehensively evaluated. This is critically needed, since in practice, most studies consider enhancers identified by only a single method. Here, we compare enhancer sets from eleven representative strategies in four biological contexts. Results All sets we evaluated overlap significantly more than expected by chance; however, there is significant dissimilarity in their genomic, evolutionary, and functional characteristics, both at the element and base-pair level, within each context. The disagreement is sufficient to influence interpretation of candidate SNPs from GWAS studies, and to lead to disparate conclusions about enhancer and disease mechanisms. Most regions identified as enhancers are supported by only one method, and we find limited evidence that regions identified by multiple methods are better candidates than those identified by a single method. As a result, we cannot recommend the use of any single enhancer identification strategy in all settings. Conclusions Our results highlight the inherent complexity of enhancer biology and identify an important challenge to mapping the genetic architecture of complex disease. Greater appreciation of how the diverse enhancer identification strategies in use today relate to the dynamic activity of gene regulatory regions is needed to enable robust and reproducible results. Electronic supplementary material The online version of this article (10.1186/s12864-019-5779-x) contains supplementary material, which is available to authorized users.

Non-coding gene regulatory loci are essential to transcription in mammalian cells. As a result, a large variety of experimental and computational strategies have been developed to identify cis-regulatory enhancer sequences. However, in practice, most studies consider enhancer candidates identified by a single method alone. Here we assess the robustness of conclusions based on such a paradigm by comparing enhancer sets identified by different strategies. Because the field currently lacks a comprehensive gold standard, our goal was not to identify the best identification strategy, but rather to quantify the consistency of enhancer sets identified by ten representative identification strategies and to assess the robustness of conclusions based on one approach alone. We found significant dissimilarity between enhancer sets in terms of genomic characteristics, evolutionary conservation, and association with functional loci. This substantial disagreement between enhancer sets within the same biological context is sufficient to influence downstream biological interpretations, and to lead to disparate scientific conclusions about enhancer biology and disease mechanisms. Specifically, we find that different enhancer sets in the same context vary significantly in their overlap with GWAS SNPs and eQTL, and that the majority of GWAS SNPs and eQTL overlap enhancers identified by only a single identification strategy. Furthermore, we find limited evidence that enhancer candidates identified by multiple strategies are more likely to have regulatory function than enhancer candidates identified by a single method. The difficulty of consistently identifying and categorizing enhancers presents a major challenge to mapping the genetic architecture of complex disease, and to interpreting variants found in patient genomes. To facilitate evaluation of the effects of different annotation approaches on studies9 conclusions, we developed a database of enhancer annotations in common biological contexts, creDB, which is designed to integrate into bioinformatics workflows. Our results highlight the inherent complexity of enhancer biology and argue that current approaches have yet to adequately account for enhancer diversity.