netbiosig2014-talk by gerald quon
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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
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