Computational personal genomics: - PowerPoint Presentation

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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

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

CATG

C

CTG

Disease-associatedvariant (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 positionsReveal individual transcription factor binding sitesWithin motif instances reveal position-specific biasMore species: motif consensus directly revealed

NRSF

motif

Chromatin state dynamics across nine cell typesSingle annotation track for each cell typeSummarize cell-type activity at a glanceCan study 9-cell activity pattern across

Correlated

activity

Predicted

linking

xx

Disease-associated SNPs enriched for enhancers in relevant cell typesE.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 studyMine publically available ENCODE data for significant hitsHundreds 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 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

Allele-specific chromatin marks: cis-vs-trans effectsMaternal and paternal GM12878 genomes sequencedMap reads to phased genome, handle SNPs indelsCorrelate activity changes with sequence differences

Brain methylation in 750 Alzheimer patients/controls

500,000

methylation

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 Bernstein

REMC mapping

Genome

Epigenome

meQTL

Phenotype

Epigenome

Classification

MWAS

1

2

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

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

Personal genomics tomorrow: Already 100,000s of complete genomesHealth, disease, quantitative traits: Genomics regions

 disease mechanism, drug targetsProtein-coding  cracking regulatory code, variation

Single genes  systems, gene interactions, pathwaysHuman ancestry: Resolve all of human ancestral relationships

Complete history of all migrations, selective eventsResolve common inheritance vs. trait associationWhat’s missing is the computationNew algorithms, machine learning, dimensionality reduction

Individualized treatment from 1000s genes, genomeUnderstand missing heritability

Reveal co-evolution between genes/elementsCorrect for modulating effects in GWAS

Collaborators and AcknowledgementsChromatin state dynamicsBrad Bernstein, ENCODE consortiumMethylation in Alzheimer’s diseasePhil de Jager, Brad Bernstein, Epigenome Roadmap

Mammalian comparative genomicsKerstin Lindblad-Toh, Eric Lander, 29 mammals consortium

Massively parallel enhancer reporter assaysTarjei Mikkelsen, Broad InstituteFunding

NHGRI, NIH, NSFSloan Foundation

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

SushmitaRoy

Luke WardStata4

Stata3Louisa

DiStefanoDave

Hendrix

Angela

Yen

Ben

Holmes

Soheil

Feizi

Mukul

Bansal

Bob

Altshuler

Stefan

Washietl

Matt

Eaton

Human constraint outside conserved regionsNon-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