Computational personal genomics: selection, regulation, epigenomics, - - PowerPoint PPT Presentation

Computational personal genomics: selection, regulation, epigenomics, disease

Manolis Kellis

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

ATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATA TATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTC TAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTC TGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACT CTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATG AATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAA GCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAAT TTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAA CTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGG TTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGAT TGATATGCTTTGCGCCGTCAAAGTTTTGAACGAGAAAAATCCATCCATTACCTTAATAAATGCTGATCCCAAATTTGCTCAAAGGAA TCGATTTGCCGTTGGACGGTTCTTATGTCACAATTGATCCTTCTGTGTCGGACTGGTCTAATTACTTTAAATGTGGTCTCCATGTTG CACTCTTTTCTAAAGAAACTTGCACCGGAAAGGTTTGCCAGTGCTCCTCTGGCCGGGCTGCAAGTCTTCTGTGAGGGTGATGTACCA TGGCAGTGGATTGTCTTCTTCGGCCGCATTCATTTGTGCCGTTGCTTTAGCTGTTGTTAAAGCGAATATGGGCCCTGGTTATCATAT CCAAGCAAAATTTAATGCGTATTACGGTCGTTGCAGAACATTATGTTGGTGTTAACAATGGCGGTATGGATCAGGCTGCCTCTGTTT GGTGAGGAAGATCATGCTCTATACGTTGAGTTCAAACCGCAGTTGAAGGCTACTCCGTTTAAATTTCCGCAATTAAAAAACCATGAA TAGCTTTGTTATTGCGAACACCCTTGTTGTATCTAACAAGTTTGAAACCGCCCCAACCAACTATAATTTAAGAGTGGTAGAAGTCAC CAGCTGCAAATGTTTTAGCTGCCACGTACGGTGTTGTTTTACTTTCTGGAAAAGAAGGATCGAGCACGAATAAAGGTAATCTAAGAG TTCATGAACGTTTATTATGCCAGATATCACAACATTTCCACACCCTGGAACGGCGATATTGAATCCGGCATCGAACGGTTAACAAAG GCTAGTACTAGTTGAAGAGTCTCTCGCCAATAAGAAACAGGGCTTTAGTGTTGACGATGTCGCACAATCCTTGAATTGTTCTCGCGA AATTCACAAGAGACTACTTAACAACATCTCCAGTGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAAT TTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATG CGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATC ATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAA GAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCA ATTGGGCAGCTGTCTATATGAATTATAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAACT AGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATA GTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGG ACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAG CTTGGCAAGTTGCCAACTGACGAGATGCAGTAAAAAGAGATTGCCGTCTTGAAACTTTTTGTCCTTTTTTTTTTCCGGGGACTCTAC GAACCCTTTGTCCTACTGATTAATTTTGTACTGAATTTGGACAATTCAGATTTTAGTAGACAAGCGCGAGGAGGAAAAGAAATGACA AAAATTCCGATGGACAAGAAGATAGGAAAAAAAAAAAGCTTTCACCGATTTCCTAGACCGGAAAAAAGTCGTATGACATCAGAATGA AATTTTCAAGTTAGACAAGGACAAAATCAGGACAAATTGTAAAGATATAATAAACTATTTGATTCAGCGCCAATTTGCCCTTTTCCA TTCCATTAAATCTCTGTTCTCTCTTACTTATATGATGATTAGGTATCATCTGTATAAAACTCCTTTCTTAATTTCACTCTAAAGCAT CCCATAGAGAAGATCTTTCGGTTCGAAGACATTCCTACGCATAATAAGAATAGGAGGGAATAATGCCAGACAATCTATCATTACATT AGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAA GTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATA GCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACA CAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATC CACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGT GTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCT

ATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATA TATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTC TAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTC TGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACT CTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATG AATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAA GCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAAT TTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAA CTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGG TTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGAT TGATATGCTTTGCGCCGTCAAAGTTTTGAACGAGAAAAATCCATCCATTACCTTAATAAATGCTGATCCCAAATTTGCTCAAAGGAA TCGATTTGCCGTTGGACGGTTCTTATGTCACAATTGATCCTTCTGTGTCGGACTGGTCTAATTACTTTAAATGTGGTCTCCATGTTG CACTCTTTTCTAAAGAAACTTGCACCGGAAAGGTTTGCCAGTGCTCCTCTGGCCGGGCTGCAAGTCTTCTGTGAGGGTGATGTACCA TGGCAGTGGATTGTCTTCTTCGGCCGCATTCATTTGTGCCGTTGCTTTAGCTGTTGTTAAAGCGAATATGGGCCCTGGTTATCATAT CCAAGCAAAATTTAATGCGTATTACGGTCGTTGCAGAACATTATGTTGGTGTTAACAATGGCGGTATGGATCAGGCTGCCTCTGTTT GGTGAGGAAGATCATGCTCTATACGTTGAGTTCAAACCGCAGTTGAAGGCTACTCCGTTTAAATTTCCGCAATTAAAAAACCATGAA TAGCTTTGTTATTGCGAACACCCTTGTTGTATCTAACAAGTTTGAAACCGCCCCAACCAACTATAATTTAAGAGTGGTAGAAGTCAC CAGCTGCAAATGTTTTAGCTGCCACGTACGGTGTTGTTTTACTTTCTGGAAAAGAAGGATCGAGCACGAATAAAGGTAATCTAAGAG TTCATGAACGTTTATTATGCCAGATATCACAACATTTCCACACCCTGGAACGGCGATATTGAATCCGGCATCGAACGGTTAACAAAG GCTAGTACTAGTTGAAGAGTCTCTCGCCAATAAGAAACAGGGCTTTAGTGTTGACGATGTCGCACAATCCTTGAATTGTTCTCGCGA AATTCACAAGAGACTACTTAACAACATCTCCAGTGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAAT TTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATG CGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATC ATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAA GAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCA ATTGGGCAGCTGTCTATATGAATTATAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAACT AGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATA GTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGG ACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAG CTTGGCAAGTTGCCAACTGACGAGATGCAGTAAAAAGAGATTGCCGTCTTGAAACTTTTTGTCCTTTTTTTTTTCCGGGGACTCTAC GAACCCTTTGTCCTACTGATTAATTTTGTACTGAATTTGGACAATTCAGATTTTAGTAGACAAGCGCGAGGAGGAAAAGAAATGACA AAAATTCCGATGGACAAGAAGATAGGAAAAAAAAAAAGCTTTCACCGATTTCCTAGACCGGAAAAAAGTCGTATGACATCAGAATGA AATTTTCAAGTTAGACAAGGACAAAATCAGGACAAATTGTAAAGATATAATAAACTATTTGATTCAGCGCCAATTTGCCCTTTTCCA TTCCATTAAATCTCTGTTCTCTCTTACTTATATGATGATTAGGTATCATCTGTATAAAACTCCTTTCTTAATTTCACTCTAAAGCAT CCCATAGAGAAGATCTTTCGGTTCGAAGACATTCCTACGCATAATAAGAATAGGAGGGAATAATGCCAGACAATCTATCATTACATT AGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAA GTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATA GCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACA CAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATC CACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGT GTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCT

Genes

Encode proteins

Regulatory motifs

Control gene expression

Recombination breakpoints

Family Inheritance Me vs. my brother

My dad Dad’s mom Mom’s dad

Human ancestry Disease risk Genomics: Regions  mechanisms  drugs Systems: genes  combinations  pathways

Personal genomics today: 23 and We

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: TFstargets, regulatorsenhancers, enhancersgenes
• 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

Tools for interpreting the human genome

• Evolutionary signatures  Genome annotation

– Distinct signatures for proteins, ncRNAs, miRNAs, motifs – Read-through, excess-constraint, networks,human selection

• Chromatin signatures  Dynamic regulatory regions

– Define chromatin states from combinations of histone marks – Distinct classes of promoter/enhancer/transcribed/repressed

• Activity signatures  Link enhancer networks

– Activity-based linking of regulators  enhancers  targets – Testing of 1000s of enhancer activator / repressor motifs

• Personal genomics  Interpret disease mechanism

– Disease-associations: Mechanistic predictions for variants – Beyond top hits: 2000+ GWAS T1D-associated enhancers – Global methylation changes in Alzheimer’s: NRSF, ELK1, CTCF

Evolutionary signatures reveal genes, RNAs, motifs

Com pare 2 9 m am m als

Increased conservation pinpoints functional regions Distinct patterns of change distinguish diff. 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

Lindblad-Toh Nature 2011 Stark Nature 2007

Compare 29 mammals: Reveal constrained positions

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

NRSF motif

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

Ward and Kellis Science 2012

Human-specific enhancer functions play regulatory roles

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

Tools for interpreting the human genome

• Evolutionary signatures  Genome annotation

– Distinct signatures for proteins, ncRNAs, miRNAs, motifs – Read-through, excess-constraint, networks,human selection

• Chromatin signatures  Dynamic regulatory regions

– Define chromatin states from combinations of histone marks – Distinct classes of promoter/enhancer/transcribed/repressed

• Activity signatures  Link enhancer networks

– Activity-based linking of regulators  enhancers  targets – Testing of 1000s of enhancer activator / repressor motifs

• Personal genomics  Interpret disease mechanism

– Disease-associations: Mechanistic predictions for variants – Beyond top hits: 2000+ GWAS T1D-associated enhancers – Global methylation changes in Alzheimer’s: NRSF, ELK1, CTCF

Integrate epigenomics datasets in multiple cell types

• Epigenetic modifications
• DNA/histone/nucleosome
• Encode epigenetic state
• Histone code hypothesis
• Distinct function for distinct

combinations of marks?

• Hundreds of histone marks
• Astronomical number of

histone mark combinations

• How do we find biologically

relevant ones?

• Unsupervised approach
• Probabilistic model
• Explicit combinatorics
• 1. Histone

modifications

• 3. DNA

accessibility

• 2. DNA

methylation

Epigenomic maps

Chromatin state 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

Correlated activity Predicted linking

Epigenomics Roadmap: 90 complete epigenomes

Interpret GWAS, global effects, reveal relevant cell types

Enhancer modules associated with tissue identity Clustering of 500,000 distal enhancers

• Tissue-specific disease-relevant tissues and processes

Dissect motifs in 10,000s of human enhancers

54000+ measurements (x2 cells, 2x repl)

• Predict activators/repressors based on activity correlations
• Validate by engineering enhancers disrupting causal motifs

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

Tools for interpreting the human genome

• Evolutionary signatures  Genome annotation

– Distinct signatures for proteins, ncRNAs, miRNAs, motifs – Read-through, excess-constraint, networks,human selection

• Chromatin signatures  Dynamic regulatory regions

– Define chromatin states from combinations of histone marks – Distinct classes of promoter/enhancer/transcribed/repressed

• Activity signatures  Link enhancer networks

– Activity-based linking of regulators  enhancers  targets – Testing of 1000s of enhancer activator / repressor motifs

• Personal genomics  Interpret disease mechanism

– Disease-associations: Mechanistic predictions for variants – Beyond top hits: 2000+ GWAS T1D-associated enhancers – Global methylation changes in Alzheimer’s: NRSF, ELK1, CTCF

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• 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

GED: Global repression of brain enhancers in AD

• Variation in methylation patterns largely genotype driven
• Global signature of repression in 1000s regulatory regions:

hypermethylation, enhancer states, brain regulator targets

Genotype (1M SNPs x700 ind.) Methylation (450k probes x 700 ind) Reference Chromatin states

Dorsolateral PFC MAP Memory and Aging Project + ROS Religious Order Study

Global hyper-methylation in 1000s of AD-associated loci

Alzheimer’s-associated probes are hypermethylated 480,000 probes, ranked by Alzheimer’s association P-value Methylation

Top 7000 probes

• Global effect across 1000s of probes

– Rank all probes by Alzheimer’s association – 7000 probes increase methylation (repressed) – Enriched in brain-specific enhancers – Near motifs of brain-specific regulators

Complex disease: genome-wide effects

Covers computational challenges associated with personal genomics:

• genotype phasing and haplotype reconstruction  resolve mom/dad chromosomes
• exploiting linkage for variant imputation  co-inheritance patterns in human population
• ancestry painting for admixed genomes  result of human migration patterns
• predicting likely causal variants using functional genomics  from regions to mechanism
• comparative genomics annotation of coding/non-coding elements  gene regulation
• relating regulatory variation to gene expression or chromatin  quantitative trait loci
• measuring recent evolution and human selection  selective pressure shaped our genome
• using systems/network information to decipher weak contributions  combinatorics
• challenge of complex multi-genic traits: height, diabetes, Alzheimer's  1000s of genes

Daniel Marbach Mike Lin Jason Ernst Jessica Wu Rachel Sealfon Pouya Kheradpour Manolis Kellis Chris Bristow Loyal Goff Irwin Jungreis

MIT Computational Biology group Compbio.mit.edu

Sushmita Roy Luke Ward

Stata4 Stata3

Louisa DiStefano Dave Hendrix Angela Yen Ben Holmes Soheil Feizi Mukul Bansal Bob Altshuler Stefan Washietl Matt Eaton