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Medically Refractory Ulcerative Colitis:
GWAS to Translation
Talin Haritunians Medical Genetics Institute
Cedars-Sinai Medical Center
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Ulcerative Colitis
Current state of genetics of UC
In a perfect world, how can GWAS
translate to the clinic?
GWAS on Medically Refractory
(medically “unresponsive”) UC
Identifying “at risk” patients
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Crohn’s Disease (CD)
Transmural Inflammation
Ileitis Ileocolitis Colitis
Ulcerative Colitis (UC)
Mucosal ulceration in the colon
Symptoms include vomiting, diarrhea, weight loss and abdominal pain.
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Hippocrates (~400BC) first description of
fistulae
“Ulcerative Colitis” defined as distinct from
infectious dysentery and nervous diarrhea
Wilks (1859)
Electric sigmoidscopy for proper diagnosis
Careful study of clinical features brought the
recognition that Ulcerative Colitis was a unique
disorder
Fenwick (1889); Dalziel (1913)
Case classification in British Medical Journal
Hawkins presented UC characteristics drawn from
85 cases collected throughout Great Britain (1909)
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Careful clinical observation to note great differences in
length of “flares”
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Amount of
inflammation
of the lining of
the colon
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Amount of
inflammation
of the lining of
the colon
Length of colon
affected
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Higher risk for family
members
Higher risk for monozygotic
twins
MZ > DZ = 1st degree
relatives >> spouses
Higher risk for some ethnic
groups (AJ)
lower risk for others (Japan)
higher in whites, lower in
blacks in South Africa
“Knockout” of several
different genes in mice causes
intestinal inflammation
First DegreeRelative
Jews BlacksJ
> 10x
~ 4x
~ 4x
Ris
k o
f IB
D
First
Degree
Relative
with IBD
NJ
Caucasian
AA
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Genetic susceptibility of patient
Mouse model knock-outs demonstrate that many genes
are possible candidates for UC
GWAS confirmed 47 UC associated loci to-date
Gut microbiota
Intestinal inflammation does not occur when mouse
models of UC are raised in a germ-free environment
IBD patients have antibodies to microbial components
Dysregulated immune response to commensal gut
bacterial flora
Transfer of various combinations of inflammatory and
regulatory T-cells increase or decrease intestinal
inflammation in mouse models
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Aminosalicylates
Mild to moderate inflammation
Corticosteroids
Moderate to severe inflammation
Non-responders to aminosalicylates
Immunomodulators
Severe inflammation
Can take 6 months for full benefit
Surgery
~20% of patients
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Meta-analysis of 6 GWAS
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Cohort Population UC cases Controls
Cedars-Sinai LA Caucasian 723 2,880
German German 1,036 1,694
CHOPSTICKS CHOP Caucasian 643 6,197
NIDDK North America 977 2,122
Swedish Sweden 948 1,408
WTCCC UK 2,360 5,417
Total 6,687 19,718
Many Cohorts
(10+)
Population UC cases Controls
Total European 9,628 12,917
Discovery
Replication
Anderson et al 2011. Nature Genetics 43:246-252.
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GWAS analysis:
6,687 cases & 19,718 controls (1.1M SNPs)
18 previously confirmed susceptibility loci
Follow-up analysis:
9,628 cases & 12,917 controls
50 novel susceptibility loci (p<1x10-5)
29 of 50 novel loci
confirmed(p<5x10-8 combined
analysis)
18 loci
~11% heritability
47 loci
~16% heritability
Anderson et al 2011. Nature Genetics 43:246-252.
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Observ
ed -
1[log(p
)]
Expected -1[log(p)]
Anderson et al 2011. Nature Genetics 43:246-252.
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Of 47 loci:
3 regions contain single gene
35 contain multiple genes
9 gene deserts
Multiple genes associated
with:
Cytokines, cytokine
receptors & key regulators of cytokine-mediated signaling
Innate & adaptive immune response
Macrophage activation
Apoptosis regulation
Intestinal barrier function (tight junction assembly in epithelial
cells)
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GNA12 Guanine nucleotide
binding protein alpha12
Membrane bound GTPase with important role
in tight junction assembly in epithelial cells
FCGR2A/B FcG receptor Transport of antigen complexes in response to
gut bacteria
CARD9 Caspase recruitment
domain 9
Adaptor molecule required for response to
pathogens, including fungi
TNFRSF14 TNF receptor
superfamily
T-cell transfer model of colitis, expression in
innate immune cells plays important role in
preventing intestinal inflammation
TNFRSF9 TNF receptor
superfamily
Involved in regulation of peripheral T-cell
activation and is expressed at sites of
inflammation
IL23R IL23 receptor Differentiation of TH17 cells
IL7R Receptor for IL7, key
regulator of naïve &
memory T-cell survival
Increased IL7R expression in T-cells in human
& murine colitis; Selective depletion of these
cells ameliorates established colitis
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Many Ulcerative Colitis patients are refractory to
medical therapy
Inflammation cannot be controlled by “mild”
therapies (aminosalicylates) nor “stronger”
therapies (IV corticosteroids, cyclosporin, or anti-
TNF biologics)
Why should we identify patients at risk of
medically refractory Ulcerative Colitis (MR-UC)?
Early introduction to more intensive therapy
Shorter time to surgery
Reduction in patient suffering
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Aminosalicylates
Mild to moderate inflammation
Corticosteroids
Moderate to severe inflammation
Non-responders to aminosalicylates
Immunomodulators
Severe inflammation
Can take 6 months for full benefit
Surgery
20% of patients
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Aminosalicylates
Mild to moderate inflammation
Corticosteroids
Moderate to severe inflammation
Non-responders to aminosalicylates
Immunomodulators
Severe inflammation
Can take 6 months for full benefit
Surgery
20% of patients
✗
✗
✗
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GWAS comparing medically responsive
& medically refractory UC (MRUC)
patients
UC requiring colectomy for symptoms
uncontrolled by medical therapy
Identify SNPs that predict UC patients
that did not respond to medical
therapies
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GWAS comparing medically responsive
& medically refractory UC
(MRUC)patients
UC requiring colectomy for symptoms
uncontrolled by medical therapy
Identify SNPs that predict UC patients
that did not respond to medical
therapies
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FACTORS
Responsive
UC
(n=537)
Refractory
MRUC
(n=324) P
Sex (F%) 47% 47% 0.89
Median Age of UC Onset (yrs) 26 27 0.93
Extraintestinal Manifestations (%) 19% 15% 0.16
Smoking (%) 8% 6% 0.24
Median Disease Duration (mo) 95 48 7.4x10-9
Extensive Disease (%) 64% 80% 2.7x10-6
Family History of UC (%) 15% 24% 0.004
Haritunians et al 2010. IBD 16:1830-40.
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FACTORS
Responsive
UC
(n=537)
Refractory
MRUC
(n=324) P
Sex (F%) 47% 47% 0.89
Median Age of UC Onset (yrs) 26 27 0.93
Extraintestinal Manifestations (%) 19% 15% 0.16
Smoking (%) 8% 6% 0.24
Median Disease Duration (mo) 95 48 7.4x10-9
Extensive Disease (%) 64% 80% 2.7x10-6
Family History of UC (%) 15% 24% 0.004
Haritunians et al 2010. IBD 16:1830-40.
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FACTORS
Responsive
UC
(n=537)
Refractory
MRUC
(n=324) P
Sex (F%) 47% 47% 0.89
Median Age of UC Onset (yrs) 26 27 0.93
Extraintestinal Manifestations (%) 19% 15% 0.16
Smoking (%) 8% 6% 0.24
Median Disease Duration (mo) 95 48 7.4x10-9
Extensive Disease (%) 64% 80% 2.7x10-6
Family History of UC (%) 15% 24% 0.004
Haritunians et al 2010. IBD 16:1830-40.
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FACTORS
Responsive
UC
(n=537)
Refractory
MRUC
(n=324) P
Sex (F%) 47% 47% 0.89
Median Age of UC Onset (yrs) 26 27 0.93
Extraintestinal Manifestations (%) 19% 15% 0.16
Smoking (%) 8% 6% 0.24
Median Disease Duration (mo) 95 48 7.4x10-9
Extensive Disease (%) 64% 80% 2.7x10-6
Family History of UC (%) 15% 24% 0.004
Haritunians et al 2010. IBD 16:1830-40.
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Genotyping Data
All samples genotyped with Illumina CNV370K chip
Samples with high rate of genotyping retained (>98%)
313,720 SNPs passed quality control
MAF >3%; HWE ≤ 0.001; SNP failure rate <10%
No differences in SNP missing data between cases and
controls
Principal component analysis used to adjust for
population stratification (Eigenstrat)
Association tested with Logistic Regression corrected
for 20 principal components (PLINK, R)
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Statistical method for handling datasets with
large number of measurements (dimensions)
by reducing these dimensions to a few PC that
explain the main patterns observed
Corrects for population stratification (the
difference in allele frequencies between cases
and controls due to ancestral differences) that
may lead to spurious association in GWAS
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Component 2
Com
pon
en
t 1
MR-UC Non-MR-UC
“Caucasian Axis”
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-log10(P
-valu
e)
2 1 3 4 5 6 7 8 9 10 11 12 13 14 15 16 22
21 20
19 18
17
Chromosome
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TNFSF15
MHC
-log10(P
-valu
e)
2 1 3 4 5 6 7 8 9 10 11 12 13 14 15 16 22
21 20
19 18
17
Chromosome
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Haritunians et al 2010. IBD 16:1830-40.
82 SNPs p<1x10-3, with
10 SNPs reaching
genome-wide significance
Association peak at 10-16
MHC association with
severe UC confirms
earlier established
association of MHC with
UC
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Haritunians et al 2010. IBD 16:1830-40.
17 SNPs p<1x10-3, with 3 SNPs
reaching genome-wide
suggestive significance
Association peak at 10-6
Increased expression observed
in inflamed mucosa of colon
and small bowel (CD)
Increased expression also
correlated with severity of ileal
and colonic inflammation in
mouse model
Neutralizing antibodies prevent
and treat established chronic
intestinal inflammation
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GWAS comparing medically responsive
& medically refractory UC
(MRUC)patients
UC requiring colectomy for symptoms
uncontrolled by medical therapy
Identify SNPs that predict UC patients
that did not respond to medical
therapies
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Each SNP was independently analyzed
Contribution of top SNPs to MR-UC was tested using forward logistic regression and Cox proportional hazards on time-to-surgery data (R)
MR-UC vs. Non-MR-UC
(n=324) (n=537)
Top 100 SNPs
(p < 3x10-4)
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Each SNP was independently analyzed
Contribution of top SNPs to MR-UC was tested using forward logistic regression and Cox proportional hazards on time-to-surgery data (R)
MR-UC vs. Non-MR-UC
(n=324) (n=537)
Top 100 SNPs
(p < 3x10-4)
37 SNPs
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Each SNP was independently analyzed
Contribution of top SNPs to MR-UC was tested using forward logistic regression and Cox proportional hazards on time-to-surgery data (R)
MR-UC vs. Non-MR-UC
(n=324) (n=537)
Top 100 SNPs
(p < 3x10-4)
37 SNPs
MR-UC (<60 mo; n=187)
vs. Non-MR-UC (n=328)
Top 65 SNPs
(p < 1x10-4)
9 SNPs
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Each SNP was independently analyzed
Contribution of top SNPs to MR-UC was tested using forward logistic regression and Cox proportional hazards on time-to-surgery data (R)
MR-UC vs. Non-MR-UC
(n=324) (n=537)
Top 100 SNPs
(p < 3x10-4)
37 SNPs
MR-UC (<60 mo; n=187)
vs. Non-MR-UC (n=328)
Top 65 SNPs
(p < 1x10-4)
9 SNPs
46 SNPs explaining 47.6% of risk for MR-UC (p-value Cox < 10-16)
+
Haritunians et al 2010. IBD 16:1830-40.
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Chr SNP Position Loci *
1 rs746503 54842574 ACOT11 | FAM151A | C1orf175 |
1 rs2275612 95140004 CNN3 | SLC44A3 |
1 rs7550055 157045388 MNDA | OR6N2 | OR2AQ1P | OR10AA1P | OR6K4P |
OR6N1 | OR6K3 | OR6K5P |
1 rs7367845 224512151 ACBD3 | MIXL1 | LIN9 |
2 rs1448901 206961885 ADAM23
2 rs4487082 229432205 2q36.3
3 rs9843732 135505746 RYK
3 rs900569 41834977 ULK4
3 rs924022 65824936 MAGI1
4 rs2286461 15572771 PROM1 | FGFBP1 | FGFBP2 | 100130067 |
4 rs12650313 41401850 LIMCH1
4 rs1399403 108639264 4q25
4 rs7675371 116049368 NDST4
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Chr SNP Position Loci *
5 rs3846599 10308821 MARCH6 | CCT5 | FAM173B | MIR378 |
5 rs6596684 105972832 5q21.3
6 rs777649 68925053 6q12
6 rs1536242 6876009 6p25.1
6 rs17207986 32187545 ATF6B | RNF5 | PPT2 | EGFL8 |
7 rs11764116 18766938 HDAC9
7 rs4722456 25338225 7p15.2
7 rs929351 81695829 CACNA2D1
8 rs2980654 6480608 ANGPT2 | AGPAT5 | MCPH1 |
8 rs6994721 76220268 8q21.11
8 rs4734754 105347978 RIMS2 | TM7SF4 |
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Chr SNP Position Loci *
9 rs7861972 6759692 JMJD2C | SNRPEL1 |
9 rs3118292 25133480 9p21.3
9 rs10817934 118589872 ASTN2
11 rs2403456 11134390 11p15.3
11 rs1461898 37546808 11p12
11 rs6591765 62674829 SLC22A24 | SLC22A25 | SLC22A10 |
12 rs887357 3344906 12p13.32
12 rs526058 24326688 12p12.1
13 rs7319358 78448935 13q31.1
14 rs1956388 28202628 14q12
14 rs11156667 30906111 GPR33 | HEATR5A | NUBPL | C14orf126 |
14 rs8020281 94436179 14q32.13
14 rs10133064 85844148 14q31.3
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Chr SNP Position Loci *
15 rs965353 57847498 BNIP2 | 100130107 | GTF2A2 |
16 rs305087 84539747 16q24.1
17 rs759258 52483547 AKAP1
19 rs2967682 8644532 MYO1F | ADAMTS10 | OR2Z1 |
19 rs2293683 12900284 CALR | DNASE2 | GCDH | FARSA | NFIX | RAD23A |
PRDX2 | KLF1 | RTBDN | GADD45GIP1 | DAND5 |
SYCE2 |
20 rs6034134 15182479 MACROD2
20 rs10485594 19772393 RIN2
20 rs6059104 31185354 PLUNC | C20orf71 | C20orf70 | C20orf186 |
21 rs2831462 28370367 21q21.3
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Risk Score as Total Count of Risk Alleles
Score is the total number of risk alleles (each of the 46 SNPs may contribute 0, 1, or 2)
Range of Observed Risk Score 28-60 Possible Range 0-92
Higher scores associated with MR-UC
108 309 69 0
1 64 199 50
28-38 39-45 46-52 53-60
MR-UC
NON-
MR-UC
SCORE
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0
20
40
60
80
100
28-38 39-45 46-52 53-60
Higher Risk Score Associated with MR-UC
28-38 39-45 46-52
Risk Score Categories
Pro
port
ion
(%
)
Non-MR-UC
MR-UC p-value Chi-squared test for trend < 10-16
53-60
Haritunians et al 2010. IBD 16:1830-40.
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Sensitivity & Specificity of Risk Score Model Following
10-fold Cross-Validation Indicate the Model was Robust
Sensitivity/ Specificity (cut-off=0.5)
Original Data with logistic regression
-----------------------------------------------------------------
0.858 Specificity
0.793 Sensitivity
-----------------------------------------------------------------
1000 times of 10 fold Cross-Validation data sets with logistic regression
--------------------------------------------------------------------------------------------------------------------
0.859 0.0021 0.858 0.870 1000 Specificity
0.789 0.0067 0.758 0.793 1000 Sensitivity
--------------------------------------------------------------------------------------------------------------------
Mean Std Dev Minimum Maximum N Variable
--------------------------------------------------------------------------------------------------------------------
Haritunians et al 2010. IBD 16:1830-40.
Detecting “true” positives
How specific is my test for particular disease?
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Time to Surgery (months)
Cu
mu
lati
ve P
robabilit
y o
f
Avoid
ing C
ole
cto
my (M
R-U
C)
39-45; n=373
53-60; n=50
46-52; n=268
28-38;
n=109
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Time to Surgery (months)
Cu
mu
lati
ve P
robabilit
y o
f
Avoid
ing C
ole
cto
my (M
R-U
C)
60
39-45; n=373
53-60; n=50
46-52; n=268
28-38;
n=109
Haritunians et al 2010. IBD 16:1830-40.
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Time to Surgery (months)
Cu
mu
lati
ve P
robabilit
y o
f
Avoid
ing C
ole
cto
my (M
R-U
C)
60
39-45; n=373
53-60; n=50
46-52; n=268
28-38;
n=109
24
8.3%
48.4%
84%
0%
19.1%
62%
Haritunians et al 2010. IBD 16:1830-40.
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Chr SNP Loci Pathway
6 rs17207986 TNXB Extracellular adhesion
6 rs3734263 UHRF1BP1 T-cell death
6 rs9470224 MAPK13,14 TCR & TLR signaling pathway
9 rs11554257 TNFSF15 Th1-Th2-Th17 activation
12 rs1144720 BICD1 Chlamydia inclusions; Antigen processing
17 rs11891 CANT1 Signal transduction
19 rs11085825 DNASE2 Lysosomal function
CALR Antigen processing
19 rs4808408 CYP4F2 Leukotriene pathway
20 rs6039206 PLCB1 Signal transduction
20 rs6059101 C20orf186 Antimicrobial peptide cluster
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BICD1
DNASE2
C20orf186
Epithelial cell
Innate/ Myeloid
T cell activation
T cell
Cytokine signaling
UHRF1BP1 CALR TNFSF15
MAPK13,14 CANT1 PLCB1
TNFSF15
TNFSF15
Potential
Therapeutic
Targets: Genes
from MR-UC
analyses
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GWAS on 861 UC patients confirmed major contribution of MHC region to UC severity
SNPs identified by GWAS may together explain a large proportion of risk 46 SNPs ~48% risk of MR-UC requiring
colectomy
Each SNP made a small contribution (OR 1.2-1.9)
Combination of risk alleles may be useful to predict medically refractive UC
Identified interesting pathways for further investigation of potential new therapeutic targets in MR-UC
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YES
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“Skip” to more aggressive therapies with
higher SNP scores.
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Cedars-Sinai Medical Center
Medical Genetics
Institute: Jerome Rotter
Kent Taylor
Talin Haritunians
Xiuqing Guo
Emebet Mengesha
Lily King
Debbie Dutridge
Dror Berel
Sheila Pressman
Inflammatory
Bowel Disease
Center: Steph Targan
Carol Landers
Dermot McGovern
Marla Dubinsky
Phillip Fleshner
Univ. of Washington
Cardiovascular
Health Research
Unit: Bruce Psaty
Josh Bis