Genomic and epigenomic signatures for interpreting complex ...compbio.mit.edu/slides/ManolisKellis_TeachersProgram.pdf · Genomic and epigenomic signatures for interpreting complex

Genomic and epigenomic signatures for interpreting complex disease

Manolis Kellis

MIT Computer Science & Artificial Intelligence Laboratory Broad Institute of MIT and Harvard

ATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTCTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTCTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACTCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATGAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAATTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAACTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGGTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGATTGATATGCTTTGCGCCGTCAAAGTTTTGAACGAGAAAAATCCATCCATTACCTTAATAAATGCTGATCCCAAATTTGCTCAAAGGAATCGATTTGCCGTTGGACGGTTCTTATGTCACAATTGATCCTTCTGTGTCGGACTGGTCTAATTACTTTAAATGTGGTCTCCATGTTGCACTCTTTTCTAAAGAAACTTGCACCGGAAAGGTTTGCCAGTGCTCCTCTGGCCGGGCTGCAAGTCTTCTGTGAGGGTGATGTACCATGGCAGTGGATTGTCTTCTTCGGCCGCATTCATTTGTGCCGTTGCTTTAGCTGTTGTTAAAGCGAATATGGGCCCTGGTTATCATATCCAAGCAAAATTTAATGCGTATTACGGTCGTTGCAGAACATTATGTTGGTGTTAACAATGGCGGTATGGATCAGGCTGCCTCTGTTTGGTGAGGAAGATCATGCTCTATACGTTGAGTTCAAACCGCAGTTGAAGGCTACTCCGTTTAAATTTCCGCAATTAAAAAACCATGAATAGCTTTGTTATTGCGAACACCCTTGTTGTATCTAACAAGTTTGAAACCGCCCCAACCAACTATAATTTAAGAGTGGTAGAAGTCACCAGCTGCAAATGTTTTAGCTGCCACGTACGGTGTTGTTTTACTTTCTGGAAAAGAAGGATCGAGCACGAATAAAGGTAATCTAAGAGTTCATGAACGTTTATTATGCCAGATATCACAACATTTCCACACCCTGGAACGGCGATATTGAATCCGGCATCGAACGGTTAACAAAGGCTAGTACTAGTTGAAGAGTCTCTCGCCAATAAGAAACAGGGCTTTAGTGTTGACGATGTCGCACAATCCTTGAATTGTTCTCGCGAAATTCACAAGAGACTACTTAACAACATCTCCAGTGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAATTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATGCGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATCATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCAATTGGGCAGCTGTCTATATGAATTATAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAACTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGCTTGGCAAGTTGCCAACTGACGAGATGCAGTAAAAAGAGATTGCCGTCTTGAAACTTTTTGTCCTTTTTTTTTTCCGGGGACTCTACGAACCCTTTGTCCTACTGATTAATTTTGTACTGAATTTGGACAATTCAGATTTTAGTAGACAAGCGCGAGGAGGAAAAGAAATGACAAAAATTCCGATGGACAAGAAGATAGGAAAAAAAAAAAGCTTTCACCGATTTCCTAGACCGGAAAAAAGTCGTATGACATCAGAATGAAATTTTCAAGTTAGACAAGGACAAAATCAGGACAAATTGTAAAGATATAATAAACTATTTGATTCAGCGCCAATTTGCCCTTTTCCATTCCATTAAATCTCTGTTCTCTCTTACTTATATGATGATTAGGTATCATCTGTATAAAACTCCTTTCTTAATTTCACTCTAAAGCATCCCATAGAGAAGATCTTTCGGTTCGAAGACATTCCTACGCATAATAAGAATAGGAGGGAATAATGCCAGACAATCTATCATTACATTAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACACAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATCCACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGTGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCT

ATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTCTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTCTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACTCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATGAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAATTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAACTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGGTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGATTGATATGCTTTGCGCCGTCAAAGTTTTGAACGAGAAAAATCCATCCATTACCTTAATAAATGCTGATCCCAAATTTGCTCAAAGGAATCGATTTGCCGTTGGACGGTTCTTATGTCACAATTGATCCTTCTGTGTCGGACTGGTCTAATTACTTTAAATGTGGTCTCCATGTTGCACTCTTTTCTAAAGAAACTTGCACCGGAAAGGTTTGCCAGTGCTCCTCTGGCCGGGCTGCAAGTCTTCTGTGAGGGTGATGTACCATGGCAGTGGATTGTCTTCTTCGGCCGCATTCATTTGTGCCGTTGCTTTAGCTGTTGTTAAAGCGAATATGGGCCCTGGTTATCATATCCAAGCAAAATTTAATGCGTATTACGGTCGTTGCAGAACATTATGTTGGTGTTAACAATGGCGGTATGGATCAGGCTGCCTCTGTTTGGTGAGGAAGATCATGCTCTATACGTTGAGTTCAAACCGCAGTTGAAGGCTACTCCGTTTAAATTTCCGCAATTAAAAAACCATGAATAGCTTTGTTATTGCGAACACCCTTGTTGTATCTAACAAGTTTGAAACCGCCCCAACCAACTATAATTTAAGAGTGGTAGAAGTCACCAGCTGCAAATGTTTTAGCTGCCACGTACGGTGTTGTTTTACTTTCTGGAAAAGAAGGATCGAGCACGAATAAAGGTAATCTAAGAGTTCATGAACGTTTATTATGCCAGATATCACAACATTTCCACACCCTGGAACGGCGATATTGAATCCGGCATCGAACGGTTAACAAAGGCTAGTACTAGTTGAAGAGTCTCTCGCCAATAAGAAACAGGGCTTTAGTGTTGACGATGTCGCACAATCCTTGAATTGTTCTCGCGAAATTCACAAGAGACTACTTAACAACATCTCCAGTGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAATTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATGCGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATCATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCAATTGGGCAGCTGTCTATATGAATTATAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAACTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGCTTGGCAAGTTGCCAACTGACGAGATGCAGTAAAAAGAGATTGCCGTCTTGAAACTTTTTGTCCTTTTTTTTTTCCGGGGACTCTACGAACCCTTTGTCCTACTGATTAATTTTGTACTGAATTTGGACAATTCAGATTTTAGTAGACAAGCGCGAGGAGGAAAAGAAATGACAAAAATTCCGATGGACAAGAAGATAGGAAAAAAAAAAAGCTTTCACCGATTTCCTAGACCGGAAAAAAGTCGTATGACATCAGAATGAAATTTTCAAGTTAGACAAGGACAAAATCAGGACAAATTGTAAAGATATAATAAACTATTTGATTCAGCGCCAATTTGCCCTTTTCCATTCCATTAAATCTCTGTTCTCTCTTACTTATATGATGATTAGGTATCATCTGTATAAAACTCCTTTCTTAATTTCACTCTAAAGCATCCCATAGAGAAGATCTTTCGGTTCGAAGACATTCCTACGCATAATAAGAATAGGAGGGAATAATGCCAGACAATCTATCATTACATTAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACACAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATCCACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGTGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCT

Genes

Encode proteins

Regulatory motifs

Control gene expression

Building systems-level views of genomes and disease

Goal: A systems-level understanding of genomes and gene regulation: • The regulators: Transcription factors, microRNAs, sequence specificities • The regions: enhancers, promoters, and their tissue-specificity • The targets: TFstargets, regulatorsenhancers, enhancersgenes • The grammars: Interplay of multiple TFs prediction of gene expression The parts list = Building blocks of gene regulatory networks Our tools: Comparative genomics & large-scale experimental datasets. • Evolutionary signatures for coding/non-coding genes, microRNAs, motifs • Chromatin signatures for regulatory regions and their tissue specificity • Activity signatures for linking regulators enhancers target genes • Predictive models for gene function, gene expression, chromatin state Integrative models = Define roles in development, health, disease

Challenge: interpreting disease-associated variants

• GWAS, case-control,… reveal disease-associated variants Molecular mechanism, cell-type specificity, drug targets

• Challenges towards interpreting disease variants – Find ‘true’ causative SNP among many candidates in LD – Use ‘causal’ variant: predict function, pathway, drug targets – Non-coding variant: type of function, cell type of activity – Regulatory variant: upstream regulators, downstream targets

• This talk: genomics tools for addressing these challenges

CATGACTG CATGCCTG

Disease-associated variant (SNP/CNV/…)

Gene annotation (Coding, 5’/3’UTR, RNAs) Evolutionary signatures

Non-coding annotation Chromatin signatures

Roles in gene/chromatin regulation Activator/repressor signatures

Other evidence of function Signatures of selection (sp/pop)

Recombination breakpoints Fa

mily

Inhe

rita

nce

Me vs. my brother

My dad Dad’s mom Mom’s dad

Hum

an a

nces

try

Dis

ease

risk

Genomics: Regions mechanisms drugs Systems: genes combinations pathways

Goal: Towards personal systems genomics

Systems-level views of disease epigenomics • Evolutionary signatures gene/genome annotation

– High-resolution annotation: genes, RNAs, motif instances – Measuring selection within the human population

• Chromatin states for interpreting disease association – Annotate dynamic regulatory elements in multiple cell types – Activity-based linking of regulators enhancers targets

• Interpreting disease-associated sequence variants – Mechanistic predictions for individual top-scoring SNPs – Functional roles of 1000s of disease-associated SNPs

• Systematic manipulation of 2000+ human enhancers – Test effect of single-motif and single-nucleotide disruptions – Role of activator/repressor motifs, disease-associated SNPs

• Personal genomes/epigenomes in health and disease – Allele-specific activity.Alzheimer’sbrain methylationSNP – Global repression of distal enhancers. NRSF, ELK1, CTCF

Large-scale comparative genomics datasets 29 mammals 17 fungi 12 flies

8 Candida

9 Yeasts

Post

-dup

licat

ion

Dip

loid

H

aplo

id

Pre-

dup

P

P

P

P

P

P

N

N

Kellis Nature 2003 Nature 2004; Stark Nature 2007; Clark Nature 2007; Butler Nature 2009; Lindblad-Toh Nature 2011

Comparative genomics and evolutionary signatures

• Comparative genomics can reveal functional elements – For example: exons are deeply conserved to mouse, chicken, fish – Many other elements are also strongly conserved: exons / regulatory?

• Can we also pinpoint specific functions of each region? Yes!

– Patterns of change distinguish different types of functional elements – Specific function Selective pressures Patterns of mutation/inse/del

• Develop evolutionary signatures characteristic of each function Kellis Nature 2003 Nature 2004; Stark Nature 2007; Clark Nature 2007; Butler Nature 2009; Lindblad-Toh Nature 2011

Evolutionary signatures for diverse functions Protein-coding genes - Codon Substitution Frequencies - Reading Frame Conservation

RNA structures - Compensatory changes - Silent G-U substitutions

microRNAs - Shape of conservation profile - Structural features: loops, pairs - Relationship with 3’UTR motifs

Regulatory motifs - Mutations preserve consensus - Increased Branch Length Score - Genome-wide conservation

Stark et al, Nature 2007

Implications for genome annotation / regulation Novel protein-coding genes Revised gene annotations Unusual gene structures

Novel structural families Targeting, editing, stability Riboswitches in mammals

Novel/expanded miR families miR/miR* arm cooperation Sense/anti-sense miR switches

Novel regulatory motifs Regulatory motif instances TF/miRNA regulatory networks Single binding site resolution

Stark et al, Nature 2007

Translational read-through in human & fly

Protein-coding conservation

Continued protein-coding conservation

No more conserv

Stop codon read through

2nd stop codon

Jungreis, Genome Research 2011

Overlapping selection in human exons

Reveal splicing signals, RNA structures, enhancer motifs, dual-coding genes

Synonym. Substitut.

Rate

Lin, Genome Research 2011

RNA structure families: ortholog/paralog cons

Ex:MAT2A S-adeosyl-methionic level detection Structure/loop sequence deep conservation

Parker Gen. Res. 2011

Regions of codon-level positive selection

Distributed vs. localized positive selection Immunity/taste vs. retinal/bone/secretion

distributed

localized

Lindblad-Toh Nature 2011

Measuring constraint at individual nucleotides

• Reveal individual transcription factor binding sites • Within motif instances reveal position-specific bias • More species: motif consensus directly revealed

NRSF motif

Detect SNPs that disrupt conserved regulatory motifs

• Functionally-associated SNPs enriched in states, constraint • Prioritize candidates, increase resolution, disrupted motifs

Measuring selection within the human lineage

Human constraint outside conserved regions

• Non-conserved regions: – ENCODE-active regions

show reduced diversity Lineage-specific constraint in

biochemically-active regions

• Conserved regions: – Non-ENCODE regions

show increased diversity Loss of constraint in human

when biochemically-inactive

Average diversity (heterozygosity) Aggregate over the genome

Active regions

Strongest: motifs, short RNA, Dnase, ChIP, lncRNA

• Significant derived allele depletion in active features

Bound motifs show increased human constraint

Position-specific reduction in bound motif heterozygosity Aggregate across thousands of CTCF motif instances

Most constrained human-specific enhancer functions

Regulatory genes: Transcription, Chromatin, Signaling. Developmental enhancers: embryo, nerve growth

Transcription initiation from Pol2 promoter Transcription coactivator activity

Transcription factor binding Chromatin binding

Negative regulation of transcription, DNA-dependent Transcription factor complex

Protein complex Protein kinase activity

Nerve growth factor receptor signaling pathway Signal transducer activity

Protein serine/threonine kinase activity Negative regulation of transcription from Pol2 prom

Protein tyrosine kinase activity In utero embryonic development







Chromatin signatures for genome annotation

Ernst et al Nature Biotech 2010

See also: Amos Tanay, Bill Noble.

2. Histone modifications

3. DNA accessibility

1. DNA methylation

Epigenomic maps

ENCODE: Study nine marks in nine human cell lines 9 human cell types 9 marks

H3K4me1

H3K4me2

H3K4me3

H3K27ac

H3K9ac

H3K27me3

H4K20me1

H3K36me3

CTCF

+WCE

+RNA

HUVEC Umbilical vein endothelial

NHEK Keratinocytes

GM12878 Lymphoblastoid

K562 Myelogenous leukemia

HepG2 Liver carcinoma

NHLF Normal human lung fibroblast

HMEC Mammary epithelial cell

HSMM Skeletal muscle myoblasts

H1 Embryonic

x

81 Chromatin Mark Tracks (281 combinations)

Ernst et al, Nature 2011

• Learned jointly across cell types (virtual concatenation)

• State definitions are common

• State locations are dynamic

Brad Bernstein ENCODE Chromatin Group

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

Predicted linking

Correlated activity

Link enhancers to target genes

Introducing multi-cell activity profiles

HUVEC NHEK GM12878 K562 HepG2

NHLF

HMEC

HSMM

H1

Gene expression

Chromatin States

Active TF motif enrichment

ON OFF

Active enhancer Repressed

Motif enrichment Motif depletion

TF regulator expression

TF On TF Off

Dip-aligned motif biases

Motif aligned Flat profile

Enhancer-gene links supported by eQTL-gene links

25

-1.4

3.2

4.4

-1.8

1.1

3.1

-1.8

-1.5

-0.5 Indiv. 1 Indiv. 2 Indiv. 3 Indiv. 4 Indiv. 5 Indiv. 6 Indiv. 7 Indiv. 8 Indiv. 9

Sequence variant at distal position

A A A C A A A C C

…

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)

Individuals

… …

Expression level of gene

15kb

• Independent validation of links.

• Relevance to disease datasets.

Validation rationale:

• Expression Quantitative Trait Loci (eQTLs) provide independent SNP-to-gene links

• Do they agree with activity-based links?

eQTL study

Visualizing 10,000s predicted enhancer-gene links

• Overlapping regulatory units, both few and many • Both upstream and downstream elements linked • Enhancers correlate with sequence constraint

26

Link TFs to target enhancers Predict activators vs. repressors

Introducing multi-cell activity profiles

HUVEC NHEK GM12878 K562 HepG2

NHLF

HMEC

HSMM

H1

Gene expression

Chromatin States

Active TF motif enrichment

ON OFF

Active enhancer Repressed

Motif enrichment Motif depletion

TF regulator expression

TF On TF Off

Dip-aligned motif biases

Motif aligned Flat profile

Ex2: Gfi1 repressor of K562/GM cells

Ex1: Oct4 predicted activator of embryonic stem (ES) cells

Coordinated activity reveals activators/repressors

• Enhancer networks: Regulator enhancer target gene

Activity signatures for each TF Enhancer activity

Causal motifs supported by dips & enhancer assays

29

Dip evidence of TF binding (nucleosome displacement)

Enhancer activity halved by single-motif disruption

Motifs bound by TF, contribute to enhancers

Tarjei Mikkelsen

Predicted causal HNF motifs (that also showed dips)

in HepG2 enhancers







Genotype Disease GWAS

Interpret variants using Epigenomics - Chromatin states: Enhancers, promoters, motifs

- Enrichment in individual loci, across 1000s of SNPs in T1D

Interpreting disease-association signals

CATGACTG CATGCCTG

Epigenome changes in disease

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 cells Lupus erythromatosus in GM lymphoblastoid

`

Creation of repressor Gfi1 motif Gain K562-specific repression Loss of enhancer function Loss of CCDC162 expression

Allele-specific chromatin marks: cis-vs-trans effects

• Maternal and paternal GM12878 genomes sequenced • Map reads to phased genome, handle SNPs indels • Correlate activity changes with sequence differences

HaploReg: systematic ENCODE mining of variants (compbio.mit.edu/HaploReg)

• Start with any list of SNPs or select a GWA study – Mine publically available ENCODE data for significant hits – Hundreds of assays, dozens of cells, conservation, motifs – Report significant overlaps and link to info/browser

Functional enrichment for 1000s of SNPs

Full T1D association spectrum 1000s of causal SNPs

GM12878 Lymphoblastoid

K562 Myelogenous leukemia

• Rank all SNPs by P-value • Find chromatin states with

enrichment in high ranks • Signal spans 1000s of SNPs

GM12878 enhancer enrichment now seen

Cell type specific: GM and K562 enhancers Chromatin state specific: Enhancers/promoters

Could bias in array design contribute to these enrichments? Evaluate all 1000 genomes SNPs by imputing those in LD

Imputing SNPs in LDstronger cell/state separation

• Excess of 30,000 SNPs2049 enhancers (excess 392) • Mostly found in independent loci (1730 with R2<0.2) Systematically measure their regulatory contributions

Enhancers across cell types Chromatin states in GM12878

Enhancers: 2049 (excess 392) 1940 distinct loci (R^2<.8)

Promoters: 462 (excess 81)

Transcribed: 4740 (excess 522)

Repressed: 1351 (excess 76)

Insulator: 240 (excess 23)

Other: 21k (deplete 1093)







High-throughput experiments: 10,000s enhancers

• Experiment features: – Multiplexed enhancer assays – 10,000s of elements – Each w/ unique barcode – Multiple human cell types – Repeat experiments on same

array / diff barcodes • Applied to:

– Test enhancer offsets – Test causal motifs

• With: Tarjei Mikkelse – Broad Institute, ARRA funds – See also: Barak Cohen,

Jay Shendure, Eran Segal Melnikov, Nature Biotech 2012

Systematic motif disruption for 5 activators and 2 repressors in 2 human cell lines

54000+ measurements (x2 cells, 2x repl)

Example activator: conserved HNF4

motif match WT expression

specific to HepG2

Non-disruptive changes maintain

expression

Motif match disruptions reduce

expression to background

Random changes depend on effect to motif match

Results hold across 2000+ enhancers

• Scramble abolishes reporter expression

• Neutral mutations show no change

• Increasing mutations show more expression

• However, only 40% show wild-type expression: context?

Features of functional wildtype enhancers

• Nucleosome exclusion, motif conservation, other TFs

• Each of these features is encoded in primary sequence

Repressors of HepG2 enhancer act in K562

Repressor disruption aberrant expression in opposite cell types

Testing effect of SNP change in enhancer constructs

• SNPs in enhancer regions can lead to expression changes in downstream reporter genes

• Currently testing all T1D-associated enhancer SNPs







Genotype Disease GWAS

(1) Interpret variants using Epigenomics - Chromatin states: Enhancers, promoters, motifs

- Enrichment in individual loci, across 1000s of SNPs in T1D

Interpreting disease-association signals

CATGACTG CATGCCTG

(2) Epigenome changes in disease - Intermediate molecular phenotypes associated with disease

- Variation in brain methylomes of Alzheimer’s patients

mQTLs MWAS Epigenome

Phil de Jager: Methylation in 750 Alzheimer patients

500,000 methylation

probes

750 individuals

• Patients followed for 10+ years with cognitive evaluations • Brain samples donated post-mortem methylation/genotype • Seek predictive features: SNPs, QTLs, mQTLs, regulation

Phil de Jager, Roadmap disease epigenomics

Brad Bernstein REMC mapping

Genome Epigenome

meQTL

Phenotype

Epigenome Classification MWAS

1 2

2,500 mQTLs for neighboring SNPs at 10-14

• Overlay Manhattan plots of 450,000 methylation probes

• Cutoff of 10-14 (10-8 after Bonferroni correction)

• Use to pinpoint disrupted motifs, predict epigenome 50

Chromosome and genomic position

P-va

lue

expo

nent

(-lo

g 10P)

Distance from CpG (MB) -1 1

Focusing on 2831 most variable probes Probe intensity distribution

Inte

r-in

divi

dual

var

iabi

lity

• Hemi-methylated probes are also the most variable

• Tiny fraction (0.6%) of all probes

• Promoters: Stable low (active)

• Gene bodies: Stable high (active)

• Enhancers/poised: Most variable

138,731

184 2,647

Multimodal probes (~3Κ)

SNP-associated probes (29% of all)

1 Active promoter

2 Promoter flanking

3 Active enhancer

4 Weak enhancer

5 Gene bodies

6 Active gene bodies

7 Repetitive

8 Heterochromatin

9 Low signal

% of CpG probes

MultimodalSNP-associatedPromoter-depleted

• SNP-associated probes depleted in promoters (driven epigenetically>genetically, open chrom)

SNP-associated All probes

• 93.5% of multimodal probes are SNP-associated

• Importance of distinguishing contribution of genotype to disease associations

Phil de Jager: Methylation in 750 Alzheimer patients

500,000 methylation

probes

750 individuals

• Patients followed for 10+ years with cognitive evaluations • Brain samples donated post-mortem methylation/genotype • Seek predictive features: SNPs, QTLs, mQTLs, regulation

Phil de Jager, Roadmap disease epigenomics

Brad Bernstein REMC mapping

Genome Epigenome

meQTL

Phenotype

Epigenome Classification MWAS

1 2

Global hyper-methylation trend in AD-associated probes

Alzheimer’s Normal

Alzheimer’s Normal

Hypomethylated probes (active)

Hypermethylated probes (repressed) Alzheimer’s-associated probes are hypermethylated

480,000 probes, ranked by Alzheimer’s association

P-v

alue

M

ethy

latio

n

Top 7000 probes

• Global effect across 1000s of probes – Rank all probes by Alzheimer’s association – Observe functional changes down ranklist – 7000 probes show shift in methylation

Complex disease: genome-wide effects, 1000s of loci

Chromatin state breakdown reveals ↓ activity

* => fisher exact test, p-value <= 0.001

% p

robe

s

1 A

ctiv

e pr

omot

er

2 Pr

omot

er

flank

ing

3 A

ctiv

e en

hanc

er

4 W

eak

enha

ncer

5 G

ene

bodi

es

6 A

ctiv

e ge

ne

bodi

es

7 Re

petit

ive

8 H

eter

ochr

omat

in

9 Lo

w s

igna

l

Red: More methylated in Alhzeimer’s Blue: Less methylated in Alzheimer’s

Significant probes are in enhancers Not promoters

Alzheimer’s prediction vs. likely biological pathways

Predictive power: 6k probes + APOE

Regulatory motifs associated with Alzheimer-associated probes

suggest potential pathways

CTCF

NRSF

ELK1

We have not solved Alzheimer’s, but new insights gained

All probes, ranked by AD assoc. P-value

All probes, ranked by AD assoc. P-value







Goal: A systems-level understanding of genomes and gene regulation: • The regulators: Transcription factors, microRNAs, sequence specificities • The regions: enhancers, promoters, and their tissue-specificity • The targets: TFstargets, regulatorsenhancers, enhancersgenes • The grammars: Interplay of multiple TFs prediction of gene expression The parts list = Building blocks of gene regulatory networks

CATGACTG CATGCCTG

Disease-associated variant (SNP/CNV/…)

Gene annotation (Coding, 5’/3’UTR, RNAs) Evolutionary signatures

Non-coding annotation Chromatin signatures

Roles in gene/chromatin regulation Activator/repressor signatures

Other evidence of function Signatures of selection (sp/pop)

Understanding human variation and human disease

• Challenge: from loci to mechanism, pathways, drug targets

Collaborators and Acknowledgements

• ENCODE – Brad Bernstein, Tarjei Mikkelsen,

Noam Shoresh, David Epstein • Massively parallel enhancer reporter assays

– Tarjei Mikkelsen, Broad Institute • Epigenome Roadmap

– Bing Ren, Brad Bernstein, John Stam, Joe Costello • 2X mammals

– Kerstin Lindblad-Toh, Eric Lander, Manuel Garber, Or Zuk • Funding

– NHGRI, NIH, NSF Sloan Foundation

Daniel Marbach

Mike Lin

Jason Ernst

Jessica Wu

Rachel Sealfon

Pouya Kheradpour

(#187) Manolis Kellis

Chris Bristow

Loyal Goff

Irwin Jungreis

MIT Computational Biology group Compbio.mit.edu

Sushmita Roy

#331: Luke Ward

Stata4 Stata3

Louisa DiStefano Dave

Hendrix

Angela Yen

Ben Holmes Soheil

Feizi Mukul Bansal

#19:Bob Altshuler

Stefan Washietl

Matt Eaton

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http://compbio.mit.edu/pictures/fullsize/StataCourtyardPanorama.html�

Documents

Genomic and epigenomic signatures for interpreting complex ...compbio.mit.edu/slides/ManolisKellis_TeachersProgram.pdf · Genomic and epigenomic signatures for interpreting complex