Transcript Yeast whole-genome analysis of conserved regulatory motifs

Chromatin state dynamics in nine human cell types
elucidate regulators and disease-associated SNPs
Jason Ernst
Joint work with Pouya Kheradpour, Luke Ward
Brad Bernstein and Manolis Kellis
Challenge:
interpreting disease-associated variants
CATGACTG
CATGCCTG
Epigenomics
Disease variants
• GWAS studies implicate thousands of non-coding loci associated
with disease
• Challenges towards interpreting disease variants:
– Find ‘true’ causative SNP among many candidates in LD
– Determining type of function: especially outside protein-coding
– Reveal relevant cell type of activity
– Link to upstream regulators and downstream targets
• This talk: chromatin tools to address these challenges
2
Challenge of data integration in many marks/cells
Construct antibodies
pull down chromatin
 ChIP-seq tracks
Epigenomic information
retains genome ‘state’
in differentiation
and development
Two types:
DNA methyl.
Histone marks
DNA packaged into
chromatin around
histone proteins
Histone tail
modifications
• Dozens of chromatin tracks
• Understand their function
• Reveal their combinations
• Annotate systematically
• Common chromatin states
• Explicitly model combinations
• Unsupervised approach,
probabilistic model
From ‘chromatin marks’ to ‘chromatin states’
Promoter states
Transcribed states
Active Intergenic
Repressed
• Learn de novo
significant
combinations of
chromatin marks
• Reveal functional
elements, even
without looking
at sequence
• Use for genome
annotation
• Use for studying
regulation
dynamics in
different cell
types
ENCODE: Study nine marks in nine human cell lines
81 Chromatin Tracks (2^81 combinations)
9 human cell types
9 marks
H3K4me1
HUVEC
Umbilical vein endothelial
H3K4me2
NHEK
Keratinocytes
GM12878
Lymphoblastoid
K562
Myelogenous leukemia
HepG2
Liver carcinoma
H4K20me1
NHLF
Normal human lung fibroblast
H3K36me3
HMEC
Mammary epithelial cell
CTCF
HSMM
Skeletal muscle myoblasts
H1
Embryonic
H3K4me3
H3K27ac
H3K9ac
H3K27me3
+WCE
+RNA
x
15 chromatin states
(for each cell type)
Chromatin states dynamics across nine cell types
• Single annotation track for each cell type
• Summarize cell-type activity at a glance
• Can study 9-cell activity pattern across
Introducing multi-cell activity profiles
Gene
expression
Chromatin
States
Active TF motif
enrichment
TF regulator
expression
Dip-aligned
motif biases
HUVEC
NHEK
GM12878
K562
HepG2
NHLF
HMEC
HSMM
H1
ON
OFF
Active enhancer
Repressed
Motif enrichment
Motif depletion
TF On
TF Off
Motif aligned
Flat profile
Linking Distal Regulatory Elements to Genes
Which gene(s) is this active enhancer in HMEC likely regulating?
?
HMEC state
IRF6
expression
-0.7
?
H3K27ac signal
-1.1
-1.7
1.2
-1.6
0.0
-1.7
-1.3
0.9
0.5
-1.6
-0.1
-1.6
0.1
4.2
0.4
3.7
0.3
Compute correlations between gene expression levels and
enhancer associated histone modification signals
C1orf107
expression
8
Linking Distal Regulatory Elements to Genes
Which gene(s) is this active enhancer in HMEC likely regulating?
Random gene
expression HMEC state
-1.1
IRF6
expression
4.0
-1.7
-0.5
-1.6
-0.8
-1.7
0.5
0.9
-0.5
-1.6
0.6
-1.6
-1.1
4.2
-1.0
3.7
Random
H3K27ac signal
-0.7
Combine intensity signal from all marks:
Train logistic regression classifier to
discriminate real from random correlations,
conditioned on state, TSS dist, cell type
Real
Compare correlations between enhancer and gene expression
between real and randomized data
9
Enhancer-gene links supported by eQTL-gene links
eQTL study
15kb
Individuals
Indiv. 1
-0.5
Indiv. 2
-1.5
Indiv. 3
Indiv. 4
-1.8
Indiv. 5
1.1
Indiv. 6
-1.8
Indiv. 7
-1.4
Indiv. 8
3.2
Indiv. 9
4.4
…
Expression
level of gene
3.1
…
A
A
A
C
A
A
A
C
C
…
Validation rationale:
• Expression Quantitative Trait Loci (eQTLs)
provide independent SNP-to-gene links
• Do they agree with activity-based links?
Example: Lymphoblastoid (GM) cells study
• Expression/genotype across 60 individuals
(Montgomery et al, Nature 2010)
• 120 eQTLs are eligible for enhancer-gene
linking based on our datasets
• 51 actually linked (43%) using predictions
 4-fold enrichment (10% exp. by chance)
Sequence variant
at distal position
• Independent validation of links.
• Relevance to disease datasets.10
Coordinated activity reveals activators/repressors
Enhancer
activity
Gene
activity
Predicted
regulators
Activity signatures for each TF
• Enhancer networks: Regulator  enhancer  target gene
• Ex1: Oct4 predicted activator of embryonic stem (ES) cells
• Ex2: Gfi1 repressor of K562/GM cells
Causal motifs supported by dips & enhancer assays
Dip evidence of TF binding
(nucleosome displacement)
Enhancer activity halved
by single-motif disruption
 Motifs bound by TF, contribute to enhancers
12
Revisiting diseaseassociated variants
xx
• Disease-associated SNPs enriched for enhancers in relevant cell types
• E.g. lupus SNP in GM enhancer disrupts Ets1 predicted activator
SNPs from GWAS Enrich for Cell Type Specific Strong Enhancer
Chromatin States in Biologically Relevant Cell Types
Cell
Type
Title
Author/
Journal
Multiple loci influence erythrocyte phenotypes in the
CHARGE Consortium.
Biological, clinical and population relevance of 95 loci for
blood lipids
Ganesh et al
Nat Genet 2009
Teslovich et al
Nature 2010
GM12878
Genome-wide association study meta-analysis identifies
seven new rheumatoid arthritis risk loci
Genome-wide meta-analyses identify three loci
associated with primary biliary cirrhosis
Stahl et al
Nat Genet 2010
Liu et al
Nat Genet 2010
GM12878
Genome-wide association study in a Chinese Han
population identifies nine new susceptibility loci for
systemic lupus erythematosus.
HepG2
Six new loci associated with blood low-density
lipoprotein cholesterol, high-density lipoprotein
cholesterol or triglycerides in humans.
K562
HepG2
GM12878
K562
K562
HepG2
K562
Genome-wide association study of hematological and
biochemical traits in a Japanese population
A genome-wide meta-analysis identifies 22 loci
associated with eight hematological parameters in the
HaemGen consortium.
Meta-analysis of genome-wide association data
identifies four new susceptibility loci for colorectal
cancer.
Genome-wide association study identifies eight loci
associated with blood pressure.
# SNPs in
Strong
enhancers
Total
#SNP
s
Fol
d
FDR
9
35
17
0.02
13
101
11
0.02
7
29
15
0.03
4
6
41
0.03
Han et al
Nat Genet 2009
6
18
21
0.03
Kathiresan et al
Nat Genet 2008
5
18
24
0.03
Kamatani et al
Nat Genet 2009
7
39
12
0.03
Soranzo et al
Nat Genet 2009
6
28
15
0.03
3
4
66
0.03
4
9
30
0.04
Houlston et al
Nat Genet 2008
Newton-Chen et al
Nat Genet 2009
Ernst et al, Nature 2011
14
Ex1: Systemic lupus erythrematosus SNP: Ets-1 motif
• SNP in lymphoblastoid GM enhancer state
• Disrupts Ets1 motif instance, predicted GM regulator
 Model: Disease SNP abolishes GM-specific enhancer
Ets-1 is a predicted activator of GM enhancers
Enhancer
activity
Gene
activity
Predicted
regulators
Activity signatures for each TF
• Ets expression  Ets-1 motif enrichment in enhancers
 Model: Ets-1 disruption would abolish enhancer state
Chromatin state dynamics: Contributions summary
• Chromatin states capture mark combinations
– Reveal promoter/enhancer/insulator/transcribed regions
• Chromatin states capture chromatin dynamics
– Single annotation track for each cell type
– Nine tracks instead of 2^81 combinations
• Activity profiles capture correlated changes
– Gene expression vs. chromatin: EnhancerGene links
– Motifs vs. TF expr vs. chromiatin: Activators/Repressors
• Regulatory predictions validated: eQTLs/dips/lucif.
– eQTLs: links. Dips: binding. Luciferase assays: motif role
• Interpret disease-associated variants
– Intergenic SNPs enriched for cell-type specific enhancers
– Mechanistic predictions reveal potential drug targets
Collaborators and Acknowledgements
MIT compbio group:
• Pouya Kheradpour
• Lucas Ward
• Manolis Kellis
ENCODE consortium
Funding
• NHGRI, NIH, NSF,
HHMI, Sloan Foundation
MGH Pathology/HHMI:
• Tarjei Mikkelsen
• Noam Shoresh
• Charles B. Epstein
• Xiaolan Zhang
• Li Wang
• Robyn Issner
• Michael Coyne
• Manching Ku
• Timothy Durham
• Bradley E. Bernstein