BLUEPRINT to decode the epigenetic signature written in blood

Adams, D. L.; Altucci, Lucia; Antonarakis, Stylianos E.; Ballesteros, J.; Beck, Stephan; Bird, Adrian; Bock, Christoph; Boehm, Bernhard O.; Campo, Elı́as; Caricasole, Andrea; Dahl, Frederik; Dermitzakis, Emmanouil T.; Enver, Tariq; Esteller, Manel; Estivill, Xavier; Ferguson-Smith, Anne C.; Fitzgibbon, Jude; Flicek, Paul; Giehl, Claudia; Graf, Thomas; Grosveld, Frank; Guigó, Roderic; Gut, Ivo; Helin, Kristian; Jarvius, Jonas; Küppers, Ralf; Lehrach, Hans; Lengauer, Thomas; Lernmark, Åke; Leslie, David W.; Loeffler, Markus; Macintyre, Elizabeth; Mai, Antonello; Martens, Joost H.A.; Minucci, Saverio; Ouwehand, Willem H.; Pelicci, Pier Giuseppe; Pendeville, Hélène; Porse, Bo T.; Rakyan, Vardham; Reik, Wolf; Schrappe, Martin; Schübeler, Dirk; Seifert, Martin; Siebert, Reiner; Simmons, David; Soranzo, Nicole; Spicuglia, Salvatore; Stratton, Michael; Stunnenberg, Hendrik G.; Tanay, Amos; Torrents, David; Valencia, Alfonso; Vellenga, Edo; Vingron, Martin; Walter, Jörn; Willcocks, Spike

doi:10.1038/nbt.2153

Cited by 316 publications

(275 citation statements)

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“…To do this, we used a publicly available active histone track (H3K4me3) from three male and three female monocyte samples, provided by the BLUEPRINT project in the UCSC Genome Browser. 32 Again, as expected, XIST showed a strong H3K4me3 signal at the promoter region, accompanied by strong expression in females only, as measured by RNA-seq (Supplementary Figure 5A). Another two regions, covering LOC286467 and near LOC642776, showed overlap between Xa probes and H3K4me3, both with an absence of gene expression differences (Supplementary Figures 5B and C).…”

Section: Xa-specific Methylation Is Associated With Genesmentioning

confidence: 72%

Human active X-specific DNA methylation events showing stability across time and tissues

et al. 2014

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The phenomenon of X chromosome inactivation in female mammals is well characterised and remains the archetypal example of dosage compensation via monoallelic expression. The temporal series of events that culminates in inactive X-specific gene silencing by DNA methylation has revealed a 'patchwork' of gene inactivation along the chromosome, with approximately 15% of genes escaping. Such genes are therefore potentially subject to sex-specific imbalance between males and females. Aside from XIST, the non-coding RNA on the X chromosome destined to be inactivated, very little is known about the extent of loci that may be selectively silenced on the active X chromosome (Xa). Using longitudinal array-based DNA methylation profiling of two human tissues, we have identified specific and widespread active X-specific DNA methylation showing stability over time and across tissues of disparate origin. Our panel of X-chromosome loci subject to methylation on Xa reflects a potentially novel mechanism for controlling female-specific X inactivation and sex-specific dimorphisms in humans. Further work is needed to investigate these phenomena.

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Section: Xa-specific Methylation Is Associated With Genesmentioning

confidence: 72%

Human active X-specific DNA methylation events showing stability across time and tissues

et al. 2014

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“…This can be addressed by large collaborative efforts such as ENCODE (The ENCODE Project Consortium 2012), BLUEPRINT (Adams et al 2012), and the Roadmap Epigenomics Project (Bernstein et al 2010). Incorporation of these genome-and epigenome-wide data sets in a multitude of different primary cell types will greatly facilitate the systematic functional interpretation of noncoding trait-associated sequence variants in terms of effector cell type and underlying molecular mechanism.…”

Section: Discussionmentioning

confidence: 99%

Maps of open chromatin highlight cell type–restricted patterns of regulatory sequence variation at hematological trait loci

et al. 2013

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Nearly three-quarters of the 143 genetic signals associated with platelet and erythrocyte phenotypes identified by metaanalyses of genome-wide association (GWA) studies are located at non-protein-coding regions. Here, we assessed the role of candidate regulatory variants associated with cell type-restricted, closely related hematological quantitative traits in biologically relevant hematopoietic cell types. We used formaldehyde-assisted isolation of regulatory elements followed by next-generation sequencing (FAIRE-seq) to map regions of open chromatin in three primary human blood cells of the myeloid lineage. In the precursors of platelets and erythrocytes, as well as in monocytes, we found that open chromatin signatures reflect the corresponding hematopoietic lineages of the studied cell types and associate with the cell typespecific gene expression patterns. Dependent on their signal strength, open chromatin regions showed correlation with promoter and enhancer histone marks, distance to the transcription start site, and ontology classes of nearby genes. Cell type-restricted regions of open chromatin were enriched in sequence variants associated with hematological indices. The majority (63.6%) of such candidate functional variants at platelet quantitative trait loci (QTLs) coincided with binding sites of five transcription factors key in regulating megakaryopoiesis. We experimentally tested 13 candidate regulatory variants at 10 platelet QTLs and found that 10 (76.9%) affected protein binding, suggesting that this is a frequent mechanism by which regulatory variants influence quantitative trait levels. Our findings demonstrate that combining large-scale GWA data with open chromatin profiles of relevant cell types can be a powerful means of dissecting the genetic architecture of closely related quantitative traits.

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“…where [1]- [4] represent the previously described settings, a g,i is the total affinity of TF i 144 for gene g, a p,i is the affinity of TF i in peak p, the set P g,x contains all open-chromatin 145 peaks in a window of size x around gene g, d p,g is the distance from the centre of peak p 146 to the TSS of gene g, s p is the scaling factor used for peak p, and d 0 is a constant fixed 147 at 5000bp [49]. TEPIC is documented using a metadata xml file [16].…”

Section: /23mentioning

confidence: 99%

“…We perform this for all peaks in [3] the 3000bp window and [4] the 50000bp window. In the remainder of the paper, we refer to [1] as the 3kb setup, to [2] as 50kb, to [3] as 3kb-S and to [4] as…”

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confidence: 99%

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

Schmidt

Gasparoni

et al. 2016

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The binding and contribution of transcription factors (TF) to cell specific gene Using machine learning, we find low affinity binding sites to improve our ability to 10 explain gene expression variability compared to the standard presence/absence 11 classification of binding sites. Further, we show that both footprints and peaks capture 12 essential TF binding events and lead to a good prediction performance. In our nearly reach the quality of several TF ChIP-seq datasets. Finally, these scores correctly 15

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BLUEPRINT to decode the epigenetic signature written in blood

Cited by 316 publications

References 3 publications

Human active X-specific DNA methylation events showing stability across time and tissues

Human active X-specific DNA methylation events showing stability across time and tissues

Maps of open chromatin highlight cell type–restricted patterns of regulatory sequence variation at hematological trait loci

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

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