A framework for identifying key regulators of complex traits

Gerald Quon

ManolisKellis

MelinaClaussnitzer

SoheilFeizi

MichalGrzadkowski

DanielMarbach

Identifying functional mechanisms of GWAS variants is challenging

chr16 position

SNP

-log 10

(p)

0

70

Reco

mbi

natio

n ra

te(c

M /

Mb)

0

100

• > 90% of GWAS variants do not tag a coding variant (Welter et al., 2014)

• Mechanism of action (target gene, or disrupted regulatory element) is typically unknown

Segrè et al. (2010) PLoS Genet 6(8): e1001058.

Rossin EJ et al. (2011) PLoS Genet 7(1): e1001273.

Most network and gene set approaches explicitly map SNPs to genes

Gene set enrichment analysis(MAGENTA)

Network enrichment analysisDAPPLE

Segrè et al. (2010) PLoS Genet 6(8): e1001058.

Rossin EJ et al. (2011) PLoS Genet 7(1): e1001273.

Most network and gene set approaches explicitly map SNPs to genes

MAGENTADAPPLE

How do we assign variants to genes?

Do physical interaction networks make sense?

Maurano et al., 2012

Few regulatory motifs are directly disrupted

GWAS variants

Card

iova

sc.

Canc

er

Cancer Cardiovasc.

Tran

scrip

tion

Fact

ors

Finding key regulators of complex traits• Goal: identify the key regulators driving complex traits (obesity

and cholesterol)

• Previous work attempt to identify regulators whose binding is directly disrupted

• We relax the constraint that regulator motifs have to be directly disrupted

• We also expand the analysis to be cell type specific

• Our networks do not depend on mapping variants to target genesTFs

Regulatory elements(blue = GWAS target)

GWAS variants

Epigenomics Roadmap profiles 127 tissues/cell types

Art: Rae Senarighi, Richard Sandstrom

Combinations of chromatin marks are associated with regulatory elements

•H3K4me3•H3K9ac•DNase

•H3K36me3•H3K79me2•H4K20me1

•H3K4me1•H3K27ac•DNase

•H3K9me3•H3K27me3•DNAmethyl

Enhancers Promoters Transcribed Repressed

FTO intron

SNP

r212

7 Ro

adm

ap c

ell t

ypes ChromHMM (Ernst et al,. 2010)

659 motifs (Kheradpour et al., 2014)(JASPAR, TRANSFAC, ENCODE)

CTCF (H1 ESC)

0 0.2 0.40

1

Prec

isio

n

Recall0.6

USF1 (H1 ESC)

0 0.10

1

Prec

isio

n

Recall0.2

Cell type specific regulatory network construction

Sum all motif instancesin a given regulatory

element

Generate shuffled motifs

Estimatebackground expected

# motif hits

Estimate # motif instances above background(threshold at 0.5)

# SN

Ps

# tagged enhancers (Liver)0 30

0

100

GWAS SNPs can tag more than one regulatory element

• ~50% of 197 total cholesterol variants tagging liver regulatory elements, tag >1 element

Regulatory elements SNPs

LD block

Lead GWAS hit

GWAS variants

Regulatory elementtargets

TFs

Infer regulators of GWAS target elements

M-step:

Refine target elementsof variants

E-step:

GWAS variants (input)

Regulatory elementtargets

Pruned GWASvariants Apply to 47 traits:

CARDIoGRAM: LDL, HDL, total cholesterol, triglycerides, CAD

GLGC: LDL, HDL, total cholesterol, triglycerides

WTCCC2: Multiple sclerosis

IBDG: Crohn’s, Ulcerative colitis

MAGIC: Glycemic traits

DIAGRAM: T2D

ICBP: Systolic and diastolic blood pressure

GIANT: BMI, weight, height

Network (input)

Subs

et o

f 127

cel

l typ

es

HD

LLD

LTo

tal c

hol.

Alzh

eim

er’s

BMI

T2D

Schi

zoph

reni

a

T2D/pancreatic islets

SZ/brain

Alzheimer’s/immune

Cholesterol/liver, adipose

BMI/adipocyte progenitors

Enriched (genome-wide+subthresh)

Enriched (subthresh 10-6)

No

enric

hen

rich

Num

ber o

f GW

AS lo

ci

Identifying relevant cell types requires 100’s of GWAS loci

PWM-regulatory element incidence matrix

111

targ

et re

gula

tory

ele

men

ts

Recurring PWMs

• More recurring PWMs comparedto previous incidence matrix fordisrupted binding elements (butalso by design.)

Total cholesterol

Example: BMI• 41 candidate regulators identified

regu

lato

r

Relative weight0 60

*

*

**

*

*

**

*

*

*

**

*

*

**

*

* *

*

*

*Implicated regulatorOf adipocyte differentiationor lipid accumulation

followup

followup

TBX15 over-expression yields decrease in lipid accumulation in isolated human adipocyte cells

• TBX15 still reduces lipid accumulation even after constitutive upregulation of a key adipocyte differentiation factor (PPARG)

• Now looking into mouse models for TBX15 knockdown as well to look for closer connection to BMI

Control TBX15++ Control TBX15++

(++PPARG)

Gesta et al., 2011

IRX3

KD

WT

IRX3 KD yields an increase in fat accumulation

• Lipid accumulation in fat cells is a clear cellular phenotype related to BMI

WT IRX3 KD

Perigonadal (visceral) fat

Whole body KD

Hypothalamus-specific KD

Adipocyte-Specific KD

Fat Mass Ratio (% of control)0 9050

IRX3 KD effect is adipocyte-specific

• Lipid accumulation is only inhibited when knocked down in adipocytes

Normal dietHigh fat diet

IRX3 KD, Normal dietIRX3 KD, High fat diet

Mobilized CD34

CD4 Memory

CD4 Naive

CD8 Naive

ATHL1

H3K4me1 signal

30kb

Predicted target genes of disrupted enhancers are involved in cholesterol related abnormalities• Distal elements linked to target promotersby correlation in activity (Jianrong Wang)

Summary

• Using only enhancer and promoter maps, we can recover known regulators and have prioritized new ones for followup

• Cell type specific regulatory networks are still far from complete– ~25% of enhancers have no predicted binding– PWM library is incomplete (~3,000+ regulators)– Linking regulatory elements to genes is still a huge

challenge

MIT Computational Biology Group

WouterMeuleman

Jason ErnstSoheil FeiziGerald QuonDaniel

Marbach

BobAltshuler

AnshulKundaje

MattEaton

AbhishekSarkar

PouyaKheradpourMariana

MendozaJessica

WuManasiVartak

DavidHendrix

MukulBansal

MattRasmussen

StefanWashietl

AndreasPfenning

HaydenMetsky

LuisBarrera

ManolisKellis

ENCODE Project Consortium Roadmap Epigenome Mapping Consortium