Transcript Personal genomics as a major focus of CSAIL research

Computational personal genomics:
selection, regulation, epigenomics, disease
Manolis Kellis
Broad Institute of MIT and Harvard
MIT Computer Science & Artificial Intelligence Laboratory
Understanding human variation and human disease
Gene annotation
(Coding, 5’/3’UTR, RNAs)
 Evolutionary signatures
Roles in gene/chromatin regulation
 Activator/repressor signatures
CATGACTG
CATGCCTG
Non-coding annotation
 Chromatin signatures
Disease-associated
variant (SNP/CNV/…)
Other evidence of function
 Signatures of selection (sp/pop)
• Challenge: from loci to mechanism, pathways, drug targets
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
Compare 29 mammals: Reveal constrained positions
NRSF
motif
• Reveal individual transcription factor binding sites
• Within motif instances reveal position-specific bias
• More species: motif consensus directly revealed
Chromatin state dynamics across nine cell types
Predicted
linking
Correlated
activity
• Single annotation track for each cell type
• Summarize cell-type activity at a glance
• Can study 9-cell activity pattern across
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
HaploReg: Automate search for any disease study
(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
Experimental dissection of regulatory motifs
for 10,000s of human enhancers
54000+ measurements (x2 cells, 2x repl)
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
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
Brain methylation in 750 Alzheimer patients/controls
750 individuals
500,000
methylation
probes
Phil de Jager, Roadmap disease epigenomics
Epigenome
Phenotype
Genome
meQTL
1
Brad Bernstein
REMC mapping
2
Epigenome
Classification
MWAS
• 10+ years of cognitive evaluations, post-mortem brains
• 93% of functional epigenomic variation is genotype driven!
• Global repression in 7,000 enhancers, brain-specific targets
Global hyper-methylation in 1000s of AD-associated loci
P-value
Top 7000 probes
Methylation
480,000 probes, ranked by Alzheimer’s association
Alzheimer’s-associated probes are hypermethylated
• Global effect across 1000s of probes
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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
Family Inheritance
Personal genomics today: 23 and We
Recombination breakpoints
Me vs.
my brother
My dad
Mom’s dad
Disease risk
Human ancestry
Dad’s mom
Genomics: Regions  mechanisms  drugs
Systems: genes  combinations  pathways
Personal genomics tomorrow:
Already 100,000s of complete genomes
• Health, disease, quantitative traits:
– Genomics regions  disease mechanism, drug targets
– Protein-coding  cracking regulatory code, variation
– Single genes  systems, gene interactions, pathways
• Human ancestry:
– Resolve all of human ancestral relationships
– Complete history of all migrations, selective events
– Resolve common inheritance vs. trait association
• What’s missing is the computation
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New algorithms, machine learning, dimensionality reduction
Individualized treatment from 1000s genes, genome
Understand missing heritability
Reveal co-evolution between genes/elements
Correct for modulating effects in GWAS
Collaborators and Acknowledgements
• Chromatin state dynamics
– Brad Bernstein, ENCODE consortium
• Methylation in Alzheimer’s disease
– Phil de Jager, Brad Bernstein, Epigenome Roadmap
• Mammalian comparative genomics
– Kerstin Lindblad-Toh, Eric Lander, 29 mammals consortium
• Massively parallel enhancer reporter assays
– Tarjei Mikkelsen, Broad Institute
• Funding
– NHGRI, NIH, NSF
Sloan Foundation
MIT Computational Biology group
Compbio.mit.edu
Mike Lin
Ben
Holmes
Angela
Yen
Matt
Eaton
Soheil
Feizi
Luke
Bob
Ward Altshuler
Stefan
Washietl
Pouya
Kheradpour
Manolis
Kellis
Jason Jessica
Ernst
Wu
Irwin
Daniel
Jungreis Marbach
Louisa
DiStefano
Sushmita
Roy
Stata3
Stata4
Chris
Bristow
Mukul
Bansal
Rachel
Sealfon
Dave
Hendrix
Loyal
Goff
Human constraint outside conserved regions
Active regions
Average
diversity
(heterozygosity)
Aggregate over
the genome
Ward and Kellis,
Science 2012
• Non-conserved regions:
• Conserved regions:
– ENCODE-active regions
show reduced diversity
 Lineage-specific constraint in
biochemically-active regions
– Non-ENCODE regions
show increased diversity
 Loss of constraint in human
when biochemically-inactive