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Computational personal genomics: selection, regulation, epigenomics,
disease
Manolis Kellis
MIT Computer Science & Artificial Intelligence Laboratory
Broad 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
Dis
ease
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: TFstargets, regulatorsenhancers, enhancersgenes• The grammars: Interplay of multiple TFs prediction of gene expression
The parts list = Building blocks of gene regulatory networks
CATGACTGCATGCCTG
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
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
NRSFmotif
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
Correlatedactivity
Predictedlinking
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
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 motifsfor 10,000s of human enhancers
54000+ measurements (x2 cells, 2x repl)
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
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
500,000methylation
probes
750 individuals
• 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
Phil de Jager, Roadmap disease epigenomics
Brad BernsteinREMC mapping
Genome Epigenome
meQTL
Phenotype
Epigenome
ClassificationMWAS
12
Global hyper-methylation in 1000s of AD-associated loci
Alzheimer’s-associated probes are hypermethylated
480,000 probes, ranked by Alzheimer’s association
P-v
alue
Met
hyla
tion
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
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 overthe genome
Active regions
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
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– 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
DanielMarbach
Mike Lin
JasonErnst
JessicaWu
RachelSealfon
PouyaKheradpour
ManolisKellis
ChrisBristow
LoyalGoff
IrwinJungreis
MIT Computational Biology groupCompbio.mit.edu
SushmitaRoy
Luke Ward
Stata4
Stata3
LouisaDiStefano Dave
Hendrix
AngelaYen
BenHolmes Soheil
FeiziMukulBansal
BobAltshuler
StefanWashietl
MattEaton