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A framework for identifying key
regulators of complex traits
Gerald Quon
Manolis
Kellis
Melina
Claussnitzer
Soheil
Feizi
M...
Identifying functional mechanisms of
GWAS variants is challenging
chr16 position
SNP-log10(p)
0
70
Recombinationrate
(cM/M...
Segrè et al. (2010) PLoS Genet 6(8): Rossin EJ et al. (2011) PLoS Genet 7(1): e1001273.
Most network and gene set approach...
Segrè et al. (2010) PLoS Genet 6(8): Rossin EJ et al. (2011) PLoS Genet 7(1): e1001273.
Most network and gene set approach...
Maurano et al., 2012
Few regulatory motifs are directly
disrupted
GWAS variants
Cardiovasc.Cancer
Cancer Cardiovasc.
Trans...
Finding key regulators of complex traits
• Goal: identify the key regulators driving complex traits (obesity and
cholester...
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
•H4...
659 motifs (Kheradpour et al., 2014)
(JASPAR, TRANSFAC, ENCODE)
CTCF (H1 ESC)
0 0.2 0.4
0
1
Precision
Recall
0.6
USF1 (H1 ...
#SNPs
# tagged enhancers (Liver)
0 30
0
100
GWAS SNPs can tag more than one
regulatory element
• ~50% of 197 total cholest...
TFs
Infer regulators of
GWAS target
elements
M-step:
Refine target
elements
of variants
E-step:
GWAS variants (input)
Regu...
Subsetof127celltypes
HDL
LDL
Totalchol.
Alzheimer’s
BMI
T2D
Schizophrenia
T2D/pancreatic islets
SZ/brain
Alzheimer’s/immun...
PWM-regulatory element incidence matrix
111targetregulatoryelements
Recurring PWMs
• More recurring PWMs compared
to previ...
Example: BMI
• 41 candidate regulators identified
regulator
Relative weight0 60
*
*
**
*
*
**
*
*
*
*
*
*
*
*
*
*
* *
*
*
...
TBX15 over-expression yields decrease in lipid
accumulation in isolated human adipocyte cells
• TBX15 still reduces lipid ...
IRX3 KD yields an increase in fat
accumulation
• Lipid accumulation in fat cells is a clear cellular phenotype
related to ...
Whole body KD
Hypothalamus-
specific KD
Adipocyte-
Specific KD
Fat Mass Ratio (% of control)
0 9050
IRX3 KD effect is adip...
Mobilized
CD34
CD4
Memory
CD4 Naive
CD8 Naive
ATHL1
H3K4me1
signal
30kb
Predicted target genes of disrupted enhancers
are ...
Summary
• Using only enhancer and promoter maps, we can
recover known regulators and have prioritized
new ones for followu...
MIT Computational Biology Group
Wouter
Meuleman
Jason ErnstSoheil FeiziGerald Quon
Daniel
Marbach
Bob
Altshuler
Anshul
Kun...
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NetBioSIG2014-Talk by Gerald Quon

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NetBioSIG2014 at ISMB in Boston, MA, USA on July 11, 2014

Published in: Science
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NetBioSIG2014-Talk by Gerald Quon

  1. 1. A framework for identifying key regulators of complex traits Gerald Quon Manolis Kellis Melina Claussnitzer Soheil Feizi Michal Grzadkowski Daniel Marbach
  2. 2. Identifying functional mechanisms of GWAS variants is challenging chr16 position SNP-log10(p) 0 70 Recombinationrate (cM/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
  3. 3. Segrè et al. (2010) PLoS Genet 6(8): 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 analysis DAPPLE
  4. 4. Segrè et al. (2010) PLoS Genet 6(8): Rossin EJ et al. (2011) PLoS Genet 7(1): e1001273. Most network and gene set approaches explicitly map SNPs to genes MAGENTA DAPPLE How do we assign variants to genes? Do physical interaction networks make sense?
  5. 5. Maurano et al., 2012 Few regulatory motifs are directly disrupted GWAS variants Cardiovasc.Cancer Cancer Cardiovasc. TranscriptionFactors
  6. 6. 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 genes TFs Regulatory elements (blue = GWAS target) GWAS variants
  7. 7. Epigenomics Roadmap profiles 127 tissues/cell types Art: Rae Senarighi, Richard Sandstrom
  8. 8. 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 SNPr2 127Roadmapcelltypes ChromHMM (Ernst et al,. 2010)
  9. 9. 659 motifs (Kheradpour et al., 2014) (JASPAR, TRANSFAC, ENCODE) CTCF (H1 ESC) 0 0.2 0.4 0 1 Precision Recall 0.6 USF1 (H1 ESC) 0 0.1 0 1 Precision Recall 0.2 Cell type specific regulatory network construction Sum all motif instances in a given regulatory element Generate shuffled motifs Estimate background expected # motif hits Estimate # motif instances above background (threshold at 0.5)
  10. 10. #SNPs # 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 element targets
  11. 11. TFs Infer regulators of GWAS target elements M-step: Refine target elements of variants E-step: GWAS variants (input) Regulatory element targets Pruned GWAS variants  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)
  12. 12. Subsetof127celltypes HDL LDL Totalchol. Alzheimer’s BMI T2D Schizophrenia T2D/pancreatic islets SZ/brain Alzheimer’s/immune Cholesterol/liver, adipose BMI/adipocyte progenitors Enriched (genome-wide+subthresh) Enriched (subthresh 10-6) Noenrich enrich NumberofGWASloci Identifying relevant cell types requires 100’s of GWAS loci
  13. 13. PWM-regulatory element incidence matrix 111targetregulatoryelements Recurring PWMs • More recurring PWMs compared to previous incidence matrix for disrupted binding elements (but also by design.) Total cholesterol
  14. 14. Example: BMI • 41 candidate regulators identified regulator Relative weight0 60 * * ** * * ** * * * * * * * * * * * * * * * Implicated regulator Of adipocyte differentiation or lipid accumulation followup followup
  15. 15. 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
  16. 16. 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
  17. 17. 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 diet High fat diet IRX3 KD, Normal diet IRX3 KD, High fat diet
  18. 18. 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 promoters by correlation in activity (Jianrong Wang)
  19. 19. 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
  20. 20. MIT Computational Biology Group Wouter Meuleman Jason ErnstSoheil FeiziGerald Quon Daniel Marbach Bob Altshuler Anshul Kundaje Matt Eaton Abhishek Sarkar Pouya KheradpourMariana Mendoza Jessica Wu Manasi Vartak David Hendrix Mukul Bansal Matt Rasmussen Stefan Washietl Andreas Pfenning Hayden Metsky Luis Barrera Manolis Kellis ENCODE Project Consortium Roadmap Epigenome Mapping Consortium

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