Transcript Manolis Kellis

Epigenomic and regulatory genomics
of complex human disease
Manolis Kellis
Broad Institute of MIT and Harvard
MIT Computer Science & Artificial Intelligence Laboratory
Family Inheritance
Personal genomics today: 23 and Me
Recombination breakpoints
Me vs.
my brother
My dad
Mom’s dad
Disease risk
Human ancestry
Dad’s mom
Genomics: Regions  mechanisms  drugs
AMD Risk
Systems: genes  combinations  pathways
Genetic
Variant
Tissue/
cell type
Heart
Muscle
Cortex
CATGACTG
CATGCCTG
Lung
Blood
Skin
Nerve
Molecular Phenotypes
Organismal
phenotypes
Gene
Epigenetic
Expression
Changes
Changes
Methyl.
Gene Endo
DNA
expr.
phenotypes
access.
Lipids
Tension
Enhancer
Gene
Heartrate Disease
expr.
Metabol.
H3K27ac
Drug resp
Promoter
Gene
expr.
Insulator
Environment
Feedback from environment / disease state
Regulatory and
systems genomics
Apply to complex
disease
1
Chromatin
states
1
Interpret
GWAS
2
Enhancer
linking
2
Epigenomics
in patients
3
Causal
Regulators
3
Disease
Networks
Epigenomics Roadmap across 100+ tissues/cell types
Art: Rae Senarighi, Richard Sandstrom
Diverse epigenomic assays:
1. Histone modifications
• H3K4me3, H3K4me1
• H3K36me3
• H3K27me3, H3K9me3
• H3K27ac, H3K9ac
2. Open chromatin:
• DNase
3. DNA methylation:
• WGBS, RRBS, MRE/MeDIP
4. Gene expression
• RNA-seq, Exon Arrays
Diverse tissues and cells:
1. Adult tissues and cells (brain, muscle, heart, digestive, skin, adipose, lung, blood…)
2. Fetal tissues (brain, skeletal muscle, heart, digestive, lung, cord blood…)
3. ES cells, iPS, differentiated cells (meso/endo/ectoderm, neural, mesench, trophobl)
Diverse chromatin signatures encode epigenomic state
Enhancers
• H3K4me1
• H3K27ac
• DNase
Promoters
• H3K4me3
• H3K9ac
• DNase
Transcribed
• H3K36me3
• H3K79me2
• H4K20me1
Repressed
• H3K9me3
• H3K27me3
• DNAmethyl
•
•
•
•
•
•
•
•
H3K4me3
H3K4me1
H3K27ac
H3K36me3
H4K20me1
H3K27me3
H3K9me3
H3K9ac
• 100s of known modifications, many new still emerging
• Systematic mapping using ChIP-, Bisulfite-, DNase-Seq
Deep sampling of 9 reference epigenomes (e.g. IMR90)
UWash Epigenome Browser, Ting Wang
Chromatin state+RNA+DNAse+28 histone marks+WGBS+Hi-C
Chromatin states capture combinations and dynamics
Predicted
linking
•
•
•
•
Correlated
activity
Single annotation track for each cell type
Capture combinations of histone marks
Summarize cell-type activity at a glance
Study activity pattern across cell types
Chromatin state annotations across 127 epigenomes
Reveal epigenomic variability: enh/prom/tx/repr/het
Anshul Kundaje
2.3M enhancer regions  only ~200 activity patterns
dev/morph
immune
muscle
morph
learning
Wouter Meuleman
<3
smooth
muscle
kidney
liver
Systematic motif dissection in 2000 enhancers:
5 activators and 2 repressors in 2 cell lines
54000+ measurements (x2 cells, 2x repl)
Kheradpour et al Genome Research 2013
Example activator:
conserved HNF4
motif match
WT expression
specific to HepG2
Motif match
disruptions reduce
expression to
background
Non-disruptive
changes maintain
expression
Random changes
depend on effect
to motif match
Regulatory and
systems genomics
Apply to complex
disease
1
Chromatin
states
1
Interpret
GWAS
2
Enhancer
linking
2
Epigenomics
in patients
3
Causal
Regulators
3
Disease
Networks
The challenge of interpreting disease-association studies
• Large associated blocks with many variants: Fine-mapping challenge
• No information on cell type/mechanism, most variants non-coding
 Epigenomic annotations help find relevant cell types / nucleotides
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
Mechanistic predictions for top disease-associated SNPs
Lupus erythromatosus in GM lymphoblastoid
Erythrocyte phenotypes in K562 leukemia cells
`
Disrupt activator Ets-1 motif
 Loss of GM-specific activation
 Loss of enhancer function
 Loss of HLA-DRB1 expression
Creation of repressor Gfi1 motif
 Gain K562-specific repression
 Loss of enhancer function
 Loss of CCDC162 expression
GWAS hits in enhancers of relevant cell types
Immune traits, heart, height, platelets, in relevant tissues
Luke Ward
Rank-based functional testing of weak associations
Enrichment peaks at 10,000s of SNPs
down the rank list, even after LD pruning!
Abhishek Sarkar
• Rank all SNPs based on GWAS signal strength
• Functional enrichment for cell types and states
Weak-effect T1D hits in 1000s T-cell enhancers
enhancers
CD4+ T-cells
T-cells
B-cells
Other cell types
Abhishek Sarkar
• Enhancer enrichment strong for top ~30k SNPs
• Heritability estimates also increase until ~30k SNPs
Brain methylation changes in AD patients
Per state: (Obs – Exp) / Total
Enhancers
Promoters
• 10,000s of methylation differences in AD vs. control
• Harbor 1000s of genetic variants associated with AD
• Localized in brain-specific enhancers and pathways
T1D/RA-enriched enhancers spread across genome
Abhishek Sarkar
• High concentration of loci in MHC, high overlap
• Yet: many distinct regions, 1000s of distinct loci
Bayesian model for joining weak SNPs in pathways
Inputs
Outputs
GWAS summary
statistics
(SNP P-values)
SNP disease-relevance
(yes/no)
Physical distances
between ncSNPs
and TSS
Gene target (if any) of
each SNP3
Interaction network
Gene disease-relevance
(yes/no)
Legend
Disease-relevant
gene
Gene near
relevant SNP
Disease-relevant
SNP
Gerald Quon
Highly ranked
SNP nearby
200 400 600 800
Poorly ranked
SNP nearby
0
0
# SNPs whose p>0
# SNPs (p>0)
1200
1200
Example 1: MAZ predicted role in T1D
00.0
0.2
0.4
0.6
0.8
p(SNP relevant)
11.0
p(SNP is disease−relevant)
10000
5000
# genes
# genes
15000
15k
0
0
0
0.0
0.2
0.4
0.6
0.8
1
1.0
p(gene relevant)
p(gene is disease−relevant)
Gerald Quon
• MAZ no direct assoc, but clusters w/ many T1D hits
• MAZ indeed known regulator of insulin expression
Example 2: SP3 predicted role in MS
300
Highly ranked
SNP nearby
50
100
200
Poorly ranked
SNP nearby
0
0
# SNPs whose p>0
# SNPs (p>0)
300
0
0.0
0.2
0.4
0.6
0.8
p(SNP relevant)
1
1.0
p(SNP is disease−relevant)
6000
4000
2000
# genes
# genes
8000
8k
0
0
0
0.0
0.2
0.4
0.6
0.8
p(gene relevant)
p(gene is disease−relevant)
1
1.0
Gerald Quon
• SP3 no direct assoc but clusters w/ many MS hits
• SP3 is indeed down-regulated in MS patients
# non-genetic hits  missing heritability
• Missing heritability partly due to weak variants
• Regulators lacking association harbor rare variants
e.g. Coronary artery disease: GATA6 (congential heart disease), HNF1A
(cardiovascular), PPARG (lipid metabolism, partial lipodystrophy)
Gerald Quon
Validate weak variant targets in model organisms
Use CRISPR/Cas to edit nucleotides, knockdown target genes
Alzheimer: Differential activity in mouse neurodegeneration
Andreas Pfenning
Cardiac: Repolarization interval in zebrafish heart
Xinchen Wang
Regulatory and
systems genomics
Apply to complex
disease
1
Chromatin
states
1
Interpret
GWAS
2
Enhancer
linking
2
Epigenomics
in patients
3
Causal
Regulators
3
Disease
Networks
Integrative analysis of 100+ epigenomes
1. Reference Epigenomes  chromatin states, linking
– Annotate dynamic regulatory elements in multiple cell types
– Activity-based linking of regulators  enhancers  targets
2. Interpreting disease-associated sequence variants
– Mechanistic predictions for individual top-scoring SNPs
– Functional roles of 1000s of disease-associated SNPs
3. Disease networks: links SNPsgenesphenotypes
– Module-based linking of enhancers to their target genes
– Bayesian model for evaluating disease genes and SNPs
4. Genetic / epigenomic variation in health and disease
– Genetic variationBrain methylationAlzheimer’s disease
– Global repression of distal enhancers. NRSF, ELK1, CTCF
MIT Computational Biology Group
Hayden
Metsky
Anshul Andreas Matt
Luis Abhishek Stefan
Kundaje Pfenning Eaton Barrera Sarkar Washietl
Bob
Altshuler
Manasi
Vartak
Daniel
Marbach
Jessica
Wu
Pouya
David
Mariana Kheradpour Hendrix
Mendoza
Matt
Rasmussen
Manolis
Mukul
Kellis
Bansal
Wouter
Meuleman
Gerald Quon Soheil Feizi
Jason Ernst
Luke Ward
Roadmap Epigenomics Integrative Analysis Team
Anshul Kundaje
Wouter Meuleman
Jason Ernst
Misha Bilenky
Lisa Chadwick
Jianrong Wang
Ting Wang
Angela Yen
John Stam
Luke Ward
Bing Ren
Cristian Coarfa, Alan Harris, Michael Ziller, Matthew
Abhishek Sarkar
Martin Hirst
Schultz, Matt Eaton, Andreas Pfenning, Xinchen Wang,
Gerald Quon
Joe Costello
Paz Polak, Rosa Karlic, Viren Amin, Yi-Chieh Wu,
Pouya Kheradpour
Brad Bernstein
Richard S Sandstrom, Zhizhuo Zhang,
Alireza Heravi-Moussavi GiNell Elliott, Rebecca Lowdon Aleks Milosavljevic