A Roadmap to the living genome - DPCPSIdpcpsi.nih.gov/council/pdf/CoC-051413-Roadmap-Living... · A...
Transcript of A Roadmap to the living genome - DPCPSIdpcpsi.nih.gov/council/pdf/CoC-051413-Roadmap-Living... · A...
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A Roadmap to the living genome
John A. Stamatoyannopoulos, M.D. Depts. of Genome Sciences & Medicine
University of Washington
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The living genome
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The living genome
‐ promoters ‐ enhancers ‐ silencers ‐ insulators ‐ etc.
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DNaseI hypersensitive sites (DHSs) mark regulatory DNA
DNaseI Hypersensitive site (DHS)
Promoters
Enhancers
~100,000 – 250,000 DHSs per cell type (0.5-1.5% of genome)
Thurman et al, Nature, 2012
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Histone modification patterns denote complex chromatin state
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Surveying the normal epigenomic landscape Adult cells and tissues
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Surveying the normal epigenomic landscape Developing cells and tissues
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Surveying the normal epigenomic landscape Embryonic stem and early progenitor cells
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A vast resource for the normal epigenome
~3,000 data sets from over 400 cellular states (cell types, differentiation states, developmental time points)
~80 highly information rich ‘complete epigenomes’ with multiple data types per cell/tissue state
DNaseI 6-30 histone modifications DNA methylation RNA
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An atlas of the living genome
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Most epigenome features are highly cell-selective
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Most epigenomic features are highly cell- and lineage-selectiv
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Most epigenomic features are highly cell- and lineage-selectiv
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Connecting epigenomic data to genes
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The epigenome and genes are densely interconnected in cis
The average enhancer is connected to 1-2 genes The average gene (promoter) is connected to 15-20 enhancers
Thurman et al. Nature 2012 Maurano et a., Science 2012
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The epigenome can ‘remember’ prior cellular
states
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Developmental persistence of enhancer chromatin accessibility
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Regulatory DNA variation associated with common
diseases and traits
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GWAS Studies Distribution of GWAS SNPs vs. genes
Identification of disease- and trait-associated variation by GWAS
Maurano et al., Science 2012
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Identification of disease- and trait-associated variation by GWAS
GWAS Studies
GWAS disease/trait associated variants x
Maps of regulatory DNA in >300 diverse cell and tissue types
Maurano et al., Science 2012
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Disease-associated variation is concentrated in
non-coding regulatory DNA
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Disease- and trait-associated SNPs are concentrated in regulatory DNA
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The effect increases monotonically with other measures of higher quality associations
Maurano et al., Science 2012
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Disease- and trait-associated SNPs are concentrated in regulatory DNA
~1.8-fold for all replicated variants in all disorders
>10-fold for specific disese-cell type pairings
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GWAS variants selectively localize in regulatory DNA of
pathologically relevant cell types
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Disease-associated variation clusters in pathogenic or target cell types
Maurano et al., Science 2012
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Cell-selective enrichment of trait-associated variants
Red blood cell traits
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Variants associated with diseases and traits with
developmental contributions preferentially localize in fetal
regulatory DNA
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Surveying the normal epigenomic landscape Developing cells and tissues
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Most variants lie in regulatory DNA of fetal origin
Maurano et al., Science 2012
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Fetal regulatory variants are enriched in traits & diseases with known links to intrauterine exposures
Maurano et al., Science 2012
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Correcting genetic variation for epigenetic circuitry
Regulatory DNA harboring disease-associated variants mainly controls
distant genes
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The epigenome and genes are densely interconnected in cis
The average enhancer is connected to 1-2 genes The average gene (promoter) is connected to 15-20 enhancers
Thurman et al. Nature 2012 Maurano et a., Science 2012
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Chromatin interactions
(ChIA-PET)
Regulatory GWAS variants linked to distant genes with causative potential
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Chromatin interactions
(ChIA-PET)
Regulatory GWAS variants linked to distant genes with causative potential
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Chromatin interactions
(ChIA-PET)
Regulatory GWAS variants linked to distant genes with causative potential
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Regulatory GWAS variants linked to distant genes with pathogenic potential
Maurano et al., Science 2012
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Disease-associated variants selectively localize to relevant
transcription factor recognition sites
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Within regulatory DNA, disease-associated variants systematically localize within relevant TF recognition sites
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DNaseI hypersensitive sites mark regulatory DNA
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Disease/trait variants specify allelic chromatin states
Overall, 20.5% of GWAS SNPs exhibit significant allelic imbalance
For those with high sequencing depth (>200x), 38.7%
Maurano et al., Science 2012
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Disease-associated variants cluster in regulatory pathways and form regulatory networks
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~25% of inflammatory disease-associated variants in regulatory
DNA perturb the JAK/STAT pathway
P < 1.6 x 10-13
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A common regulatory network for autoimmune disease
Maurano et al., Science 2012
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Maurano et al., Science 2012
A common regulatory network underlies diverse malignancies
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Epigenomic data enable pinpointing of disease/trait-
relevant cell types
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Cell-selective enrichment of trait-associated variants
Red blood cell traits
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Selective enrichment of GWAS variants in pathogenic cell types
Maurano et al., Science 2012
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Selective enrichment of GWAS variants in pathogenic cell types
Maurano et al., Science 2012
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Summary & Implications The Roadmap Epigenomics Project has created a vast, high-quality atlas of the epigenomic states of normal cells and tissues A powerful, enabling resource for diverse investigators
Roadmap data can be integrated to reveal important insights into cellular phenotypes and functions Many novel features of the data await exploration and discovery
Disease-associated variation is concentrate in regulatory DNA Enables a coherent approach to understanding the role of non-coding variants Reference maps of normal cells enable pathogenic insights Reference maps are a powerful tool that, when combined with genetic data, may obviate the need to perform deep profiling of disease populations
Although it has covered significant ground, the Roadmap is only a start Only a fraction of the true diversity of human cell types and states has been covered
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Acknowledgements
Analysis Data production Matthew Maurano, Rich Humbert, Eric Rynes Raj Kaul, Scott Hansen, Peter Sabo, Eric Haugen, Richard Sandstrom, Audra Johnson, Molly Weaver, Theresa Cantwell, Kristin Eric Haugen, Alex Reynolds, Bob Thurman Lee, Shinny Vong, Vaughan Roach, Erica
Gist, Sandra Stehling‐Sun
Collaborators Ian Glass (UW Birth Def. Res. Lab) Shelly Heimfeld (FHCRC) Nona Sotoodehia and Jen Brody (UW Cardiology) Chris Cotsapas (Yale) Shamil Sunyaev (Harvard)
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Acknowledgements