Epigenomic and regulatory genomics of complex human disease
description
Transcript of Epigenomic and regulatory genomics of complex human disease
Epigenomic and regulatory genomics of complex human disease
Manolis Kellis
MIT Computer Science & Artificial Intelligence LaboratoryBroad Institute of MIT and Harvard
Recombination breakpointsFa
mily
Inhe
ritan
ce
Me vs. my brother
My dadDad’s mom Mom’s dad
Hum
an a
nces
try
Dise
ase
risk
Genomics: Regions mechanisms drugs Systems: genes combinations pathways
Personal genomics today: 23 and Me
AMD Risk
CATGACTGCATGCCTG
GeneticVariant
Disease
Methyl.
DNAaccess.
Enhancer
H3K27ac
Promoter
Insulator
EpigeneticChanges
Geneexpr.
Molecular Phenotypes
Geneexpr.
Geneexpr.
GeneExpression
Changes
Muscle
Heart
Cortex
Lung
Blood
Skin
Nerve
Tissue/cell type
Organismalphenotypes
LipidsTensionHeartrateMetabol.Drug resp
Endophenotypes
Feedback from environment / disease state
Environment
1
2
Causal Regulators3
Chromatin states
Enhancer linking
Regulatory andsystems genomics
Apply to complex disease
Interpret GWAS
Epigenomics in patients
1
2
3 DiseaseNetworks
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)
Epigenomics Roadmap across 100+ tissues/cell types
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
Art: Rae Senarighi, Richard Sandstrom
Diverse chromatin signatures encode epigenomic state
• 100s of known modifications, many new still emerging• Systematic mapping using ChIP-, Bisulfite-, DNase-Seq
• H3K4me3• H3K9ac• DNase
• H3K36me3• H3K79me2• H4K20me1
• H3K4me1• H3K27ac• DNase
• H3K9me3• H3K27me3• DNAmethyl
• H3K4me3• H3K4me1• H3K27ac• H3K36me3• H4K20me1• H3K27me3• H3K9me3• H3K9ac
Enhancers Promoters Transcribed Repressed
Deep sampling of 9 reference epigenomes (e.g. IMR90)
Chromatin state+RNA+DNAse+28 histone marks+WGBS+Hi-CUWash Epigenome Browser, Ting Wang
Chromatin states capture combinations and dynamics
• 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
Correlatedactivity
Predictedlinking
Chromatin state annotations across 127 epigenomes
Reveal epigenomic variability: enh/prom/tx/repr/hetAnshul Kundaje
2.3M enhancer regions only ~200 activity patterns
Wouter Meuleman
immunedev/morph
morphlearning
muscle
<3smoothmuscle
kidney
liver
54000+ measurements (x2 cells, 2x repl)
Kheradpour et al Genome Research 2013
Systematic motif dissection in 2000 enhancers: 5 activators and 2 repressors in 2 cell lines
Example activator: conserved HNF4 motif
matchWT expression
specific to HepG2
Non-disruptive changes maintain
expression
Motif match disruptions reduce
expression to background
Random changes depend on effect to motif match
1
2
Causal Regulators3
Chromatin states
Enhancer linking
Regulatory andsystems genomics
Apply to complex disease
Interpret GWAS
Epigenomics in patients
1
2
3 DiseaseNetworks
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
xx
• Disease-associated SNPs enriched for enhancers in relevant cell types• E.g. lupus SNP in GM enhancer disrupts Ets1 predicted activator
Revisiting disease- associated variants
Mechanistic predictions for top disease-associated SNPs
Disrupt activator Ets-1 motif Loss of GM-specific activation Loss of enhancer function Loss of HLA-DRB1 expression
Erythrocyte phenotypes in K562 leukemia cellsLupus erythromatosus in GM lymphoblastoid
`
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 tissuesLuke Ward
Rank-based functional testing of weak associations
• Rank all SNPs based on GWAS signal strength• Functional enrichment for cell types and states
Enrichment peaks at 10,000s of SNPsdown the rank list, even after LD pruning!
Abhishek Sarkar
Weak-effect T1D hits in 1000s T-cell enhancers
• Enhancer enrichment strong for top ~30k SNPs• Heritability estimates also increase until ~30k SNPs
enhancersCD4+ T-cells
T-cellsB-cells
Other cell types
Abhishek Sarkar
Per s
tate
: (O
bs –
Exp
) / To
tal
Enhancers
Promoters
Brain methylation changes in AD patients
• 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
• High concentration of loci in MHC, high overlap• Yet: many distinct regions, 1000s of distinct loci
Abhishek Sarkar
Bayesian model for joining weak SNPs in pathways
Inputs OutputsGWAS summary statistics(SNP P-values)
Interaction network
Physical distances between ncSNPs and TSS
SNP disease-relevance (yes/no)
Gene disease-relevance(yes/no)
Gene target (if any) of each SNP3
Disease-relevantgeneLegend Gene near
relevant SNPDisease-relevantSNP
Gerald Quon
Poorly ranked SNP nearby
Highly rankedSNP nearby
0 1p(SNP relevant)
# SN
Ps (p
>0)
0
1200
0 1p(gene relevant)
# ge
nes
0
15k
Example 1: MAZ predicted role in T1D
• MAZ no direct assoc, but clusters w/ many T1D hits• MAZ indeed known regulator of insulin expression
Gerald Quon
Example 2: SP3 predicted role in MSPoorly ranked SNP nearby
Highly rankedSNP nearby
0 1p(SNP relevant)
0 1p(gene relevant)
# SN
Ps (p
>0)
0
300
# ge
nes
0
8k
• SP3 no direct assoc but clusters w/ many MS hits• SP3 is indeed down-regulated in MS patients
Gerald Quon
# non-genetic hits missing heritability
Gerald Quon
• 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)
Validate weak variant targets in model organisms
Use CRISPR/Cas to edit nucleotides, knockdown target genesAlzheimer: Differential activity in mouse neurodegenerationCardiac: Repolarization interval in zebrafish heart Andreas Pfenning
Xinchen Wang
1
2
Causal Regulators3
Chromatin states
Enhancer linking
Regulatory andsystems genomics
Apply to complex disease
Interpret GWAS
Epigenomics in patients
1
2
3 DiseaseNetworks
Integrative analysis of 100+ epigenomes1. 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 SNPsgenesphenotypes– 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 variationBrain methylationAlzheimer’s disease– Global repression of distal enhancers. NRSF, ELK1, CTCF
MIT Computational Biology Group
WouterMeuleman
Jason ErnstLuke Ward
Soheil FeiziGerald QuonDaniel
Marbach
BobAltshuler
AnshulKundaje
MattEaton
AbhishekSarkar
PouyaKheradpour
MIT Computational Biology Group
MarianaMendoza
JessicaWu
ManasiVartak
DavidHendrix
MukulBansal
MattRasmussen
StefanWashietl
AndreasPfenning
HaydenMetsky
LuisBarrera
ManolisKellis
Roadmap Epigenomics Integrative Analysis Team
Lisa ChadwickTing WangJohn Stam
Bing RenMartin Hirst
Joe CostelloBrad Bernstein
Aleks Milosavljevic
Anshul KundajeWouter MeulemanJason ErnstMisha BilenkyJianrong WangAngela YenLuke WardAbhishek SarkarGerald QuonPouya KheradpourAlireza Heravi-Moussavi
Cristian Coarfa, Alan Harris, Michael Ziller, Matthew Schultz, Matt Eaton, Andreas Pfenning, Xinchen Wang,
Paz Polak, Rosa Karlic, Viren Amin, Yi-Chieh Wu, Richard S Sandstrom, Zhizhuo Zhang,
GiNell Elliott, Rebecca Lowdon