University of Groningen
Association analyses identify 38 susceptibility loci for inflammatory bowel disease and
highlight shared genetic risk across populations
Liu, Jimmy Z.; van Sommeren, Suzanne; Huang, Hailiang; Ng, Siew C.; Alberts, Rudi;
Takahashi, Atsushi; Ripke, Stephan; Lee, James C.; Jostins, Luke; Shah, Tejas
Published in:
Nature Genetics
DOI:
10.1038/ng.3359
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Liu, J. Z., van Sommeren, S., Huang, H., Ng, S. C., Alberts, R., Takahashi, A., Ripke, S., Lee, J. C.,
Jostins, L., Shah, T., Abedian, S., Cheon, J. H., Cho, J., Daryani, N. E., Franke, L., Fuyuno, Y., Hart, A.,
Juyal, R. C., Juyal, G., ... Int IBD Genetics Consortium (2015). Association analyses identify 38
susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations.
Nature Genetics
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Nature GeNetics VOLUME 47 | NUMBER 9 | SEPTEMBER 2015 979
IBD is composed of chronic, relapsing intestinal inflammatory dis-
eases affecting more than 2.5 million people in Europe, with increasing
prevalence in Asia and developing countries
1,2
. IBD is thought to arise
from inappropriate activation of the intestinal mucosal immune system
in response to commensal bacteria in a genetically susceptible host.
Thus far, 163 genetic loci have been associated with IBD via large-
scale genome-wide association studies (GWAS) in cohorts of European
descent. Smaller GWAS performed in populations from Japan, India
and Korea have reported six new genome-wide significant associations
outside of the human leukocyte antigen (HLA) region. Three of these
loci (13q12, FCGR2A and SLC26A3) subsequently achieved genome-
wide significant evidence of association in European cohorts. The
remaining three loci demonstrated a consistent direction of effect
and nominally significant evidence of association (P < 1 × 10
−4
) in
previous European GWAS analyses
3–6
. A number of loci initially asso-
ciated with IBD in European cohorts have now also been shown to
underlie risk in non-Europeans, including JAK2, IL23R and NKX2-3.
The evidence of shared IBD risk loci across diverse populations
Association analyses identify 38 susceptibility loci for
inflammatory bowel disease and highlight shared genetic
risk across populations
Jimmy Z Liu
1,25
, Suzanne van Sommeren
2,3,25
, Hailiang Huang
4
, Siew C Ng
5
, Rudi Alberts
2
, Atsushi Takahashi
6
,
Stephan Ripke
4
, James C Lee
7
, Luke Jostins
8
, Tejas Shah
1
, Shifteh Abedian
9
, Jae Hee Cheon
10
, Judy Cho
11
,
Naser E Daryani
12
, Lude Franke
3
, Yuta Fuyuno
13
, Ailsa Hart
14
, Ramesh C Juyal
15
, Garima Juyal
16
, Won Ho Kim
10
,
Andrew P Morris
17
, Hossein Poustchi
9
, William G Newman
18
, Vandana Midha
19
, Timothy R Orchard
20
,
Homayon Vahedi
9
, Ajit Sood
19
, Joseph J Y Sung
5
, Reza Malekzadeh
9
, Harm-Jan Westra
3
, Keiko Yamazaki
13
,
Suk-Kyun Yang
21
, International Multiple Sclerosis Genetics Consortium
22
, International IBD Genetics Consortium
22
,
Jeffrey C Barrett
1
, Andre Franke
23
, Behrooz Z Alizadeh
24
, Miles Parkes
7
, Thelma B K
16
, Mark J Daly
4
,
Michiaki Kubo
13,26
, Carl A Anderson
1,26
& Rinse K Weersma
2,26
Ulcerative colitis and Crohn’s disease are the two main forms of inflammatory bowel disease (IBD). Here we report the first trans-
ancestry association study of IBD, with genome-wide or Immunochip genotype data from an extended cohort of 86,640 European
individuals and Immunochip data from 9,846 individuals of East Asian, Indian or Iranian descent. We implicate 38 loci in IBD risk for
the first time. For the majority of the IBD risk loci, the direction and magnitude of effect are consistent in European and non-European
cohorts. Nevertheless, we observe genetic heterogeneity between divergent populations at several established risk loci driven by
differences in allele frequency (NOD2) or effect size (TNFSF15 and ATG16L1) or a combination of these factors (IL23R and IRGM).
Our results provide biological insights into the pathogenesis of IBD and demonstrate the usefulness of trans-ancestry association
studies for mapping loci associated with complex diseases and understanding genetic architecture across diverse populations.
1
Wellcome Trust Sanger Institute, Hinxton, UK.
2
Department of Gastroenterology and Hepatology, University of Groningen, University Medical Center Groningen,
Groningen, the Netherlands.
3
Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
4
Analytic and
Translational Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
5
Department of Medicine and Therapeutics,
Institute of Digestive Disease, LKS Institute of Health Science, State Key Laboratory of Digestive Disease, Chinese University of Hong Kong, Hong Kong.
6
Laboratory
for Statistical Analysis, Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan.
7
Inflammatory Bowel Disease Research Group, Addenbrooke’s Hospital,
Cambridge, UK.
8
Wellcome Trust Centre for Human Genetics, University of Oxford, Headington, UK.
9
Digestive Disease Research Institute, Shariati Hospital, Tehran,
Iran.
10
Department of Gastroenterology and Hepatology, Yonsei University College of Medicine, Seoul, Korea.
11
Icahn School of Medicine, Mount Sinai Hospital,
New York, New York, USA.
12
Department of Gastroenterology, Emam Hospital, Tehran, Iran.
13
Laboratory for Genotyping Development, Center for Integrative Medical
Sciences, RIKEN, Yokohama, Japan.
14
Inflammatory Bowel Disease Unit, St Mark’s Hospital, Harrow, UK.
15
National Institute of Immunology, New Delhi, India.
16
Department of Genetics, University of Delhi South Campus, New Delhi, India.
17
Department of Biostatistics, University of Liverpool, Liverpool.
18
Manchester
Centre for Genomic Medicine, University of Manchester and Central Manchester University Hospitals National Health Service (NHS) Foundation Trust, Manchester,
UK.
19
Department of Medicine, Dayanand Medical College and Hospital, Ludhiana, India.
20
Department of Gastroenterology and Hepatology, St. Mary’s Hospital,
London, UK.
21
Department of Gastroenterology and Hepatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
22
A full list of members
and affiliations appears in the Supplementary Note.
23
Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany.
24
Department of
Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
25
These authors contributed equally to this work.
26
These authors jointly supervised this work. Correspondence should be addressed to R.K.W. (r.k.weersma@umcg.nl) or C.A.A. (carl.anderson@sanger.ac.uk).
Received 30 October 2014; accepted 24 June 2015; published online 20 July 2015; doi:10.1038/ng.3359
A R T I C L E S
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980 VOLUME 47 | NUMBER 9 | SEPTEMBER 2015 Nature GeNetics
A R T I C L E S
suggests that combining genotype data from cohorts of different
ancestry will enable the detection of additional IBD-associated loci.
Such trans-ancestry association studies have successfully identi-
fied susceptibility loci for other complex diseases, including type 2
diabetes and rheumatoid arthritis
7,8
.
In this study, we aggregate genome-wide or Immunochip genotype
data from 96,486 individuals. In comparison to our previously
published GWAS meta-analysis, this study includes an additional
11,535 individuals of European ancestry and 9,846 individuals of
non-European ancestry. Using these data, we aim to identify new IBD
risk loci and compare the genetic architecture of IBD susceptibility
across ancestrally divergent populations.
RESULTS
Study design
After quality control and 1000 Genomes Project imputation (Phase I–
August 2012), we used 5,956 Crohn’s disease cases, 6,968 ulcerative
colitis cases and 21,770 population controls of European descent to
perform GWAS of Crohn’s disease, ulcerative colitis and IBD (Crohn’s
disease and ulcerative colitis together) (Online Methods). Replication
was undertaken using an additional 16,619 Crohn’s disease cases, 13,449
ulcerative colitis cases and 31,766 population controls genotyped
on the Immunochip. The replication cohort included 2,025 Crohn’s
disease cases, 2,770 ulcerative colitis cases and 5,051 population
controls of non-European ancestry (Table 1 and Supplementary
Figs. 1 and 2), so principal-component analysis was used to assign
individuals to 1 of 4 ancestral groups (European, Iranian, Indian or
East Asian) (Supplementary Fig. 3). Case-control association tests
were performed within each ancestry group using a linear mixed
model (MMM)
9
(Online Methods). A fixed-effects meta-analysis was
undertaken to combine the summary statistics from our European-
only GWAS meta-analysis with those from the European replication
cohort. We next performed a Bayesian trans-ancestry meta-analysis,
as implemented in MANTRA, to enable heterogeneity in effect sizes
to be correlated with the genetic distance between populations, as
estimated by the mean fixation index (F
ST
) across all SNPs
10
(Online
Methods). For the trans-ancestry meta-analysis, the 6,392 cases and
7,262 population controls of European ancestry that were present
in both the GWAS and replication cohorts were excluded from the
Immunochip replication study (Supplementary Fig. 2). To maximize
power for our solely Immunochip-based comparisons across ances-
tral groups, the mixed-model association analysis was repeated after
reinstating these individuals in the Immunochip cohort.
Trans-ancestry meta-analysis identifies 38 new IBD loci
In total, 38 new disease-associated loci were identified at genome-
wide significance in either the association analysis of individual
ancestry groups (P < 5 × 10
−8
) or the trans-ancestry meta-analysis that
included all ancestries (log
10
(Bayes factor) > 6) for ulcerative colitis,
Crohn’s disease or IBD (Table 2, Supplementary Figs. 4–7 and
Supplementary Tables 1 and 2). To reduce false positive associations,
we required all loci only implicated in disease risk via the trans-
ancestry meta-analysis (with log
10
(Bayes factor) > 6 but P > 5 × 10
−8
in each individual ancestry cohort) to show no significant evi-
dence of heterogeneity across all four ancestry groups (I
2
> 85.7%)
(Online Methods and Supplementary Table 3).
Twenty-five of the 38 newly associated loci overlapped with loci
previously reported for other traits, including immune-mediated
diseases, whereas 13 had not previously been associated with any
disease or trait (Online Methods and Supplementary Table 4).
A likelihood-modeling approach showed that 27 of the 38 newly
identified loci were associated with both Crohn’s disease and ulcera-
tive colitis (designated here as IBD-associated loci), with 7 of these
loci demonstrating evidence of heterogeneity of effect between the 2
diseases. Of the remaining 11 loci, 7 were classified as specific to
Crohn’s disease and 4 were classified as specific to ulcerative colitis
(Table 2 and Supplementary Table 1).
As a result of our updated sample quality control procedure, 17 of
the 194 independent SNPs reported at genome-wide significance in
our previous European-only GWAS meta-analysis
6
failed to reach this
significance threshold in the present study. Sixteen of these loci still
demonstrated strong suggestive evidence of association in the current
European cohort (5 × 10
−8
< P < 8.7 × 10
−6
, representing a false discov-
ery rate (FDR) of ~0.001) (Supplementary Table 1). SNP rs2226628 on
chromosome 11 failed to achieve even suggestive evidence of associa-
tion in our current European association analysis (P = 0.0024). Our pre-
vious European-only meta-analysis incorporated a number of principal
components as covariates in a logistic regression test of association,
and, interestingly, if we adopted the approach taken by Jostins et al.
6
,
we observed a more significant P value of 7.38 × 10
−6
for this SNP.
This observation, together with the divergent allele frequencies at this
SNP across European populations (1000 Genomes Project release 14:
GBR (British in England and Scotland), 0.20; CEU (Utah residents of
Western European ancestry), 0.28; IBS (Spanish Iberian), 0.39; FIN
(Finnish), 0.47) suggests that the previously reported signal of associa-
tion might have been driven, at least in part, by population stratification
(which is now better accounted for in the linear mixed-model analy-
sis)
6
. In summary, we now consider 231 independent SNPs within
200 loci to be associated with IBD risk (Supplementary Table 2).
Forty-one of the 163 IBD-associated SNPs originally identified in
our previous European-only GWAS meta-analysis replicated in at least
one non-European cohort if we considered a one-tailed Bonferroni-
corrected significance threshold of P < 6.1 × 10
−4
(0.05/163)
(Supplementary Table 1). Nine of the 14 non-HLA loci (10 for
Crohn’s disease and 4 for ulcerative colitis) that had been identified at
genome-wide levels of significance in previous non-European GWAS
cohorts from Japan, India and Korea
3,4,11–13
were associated with
either Crohn’s disease or ulcerative colitis in the East Asian, Indian
and/or Iranian cohorts with P < 1.0 × 10
−5
(Supplementary Table 5).
Four of the five remaining SNPs (or reliable proxy SNPs) were not
present on the Immunochip. The previously reported association
at rs2108225 (SLC26A3) on chromosome 7 showed an association sig-
nal at P = 2.64 × 10
−3
in the current East Asian cohort but was strongly
associated with IBD in the European cohort (P = 1.04 × 10
−18
).
We next performed a series of analyses to prioritize genes within
the newly associated loci for causality. Cis-eQTL (expression quantita-
tive trait locus) analysis from two data sets of peripheral blood sam-
ples from a total of 1,240 individuals showed that 12 of the 38 newly
associated SNPs had cis-eQTL effects (FDR < 0.05) (Online Methods
and Supplementary Table 6). Two SNPs showed trans-eQTL effects.
Table 1 Cohort sample sizes for GWAS and Immunochip
trans-ancestry meta-analysis
Population Crohn’s disease Ulcerative colitis IBD
Cases Controls Cases Controls Cases Controls
European
GWAS
5,956 14,927 6,968 20,464 12,882 21,770
European
Immunochip
14,594 26,715 10,679 26,715 25,273 26,715
Non-European
Immunochip
2,025 5,051 2,770 5,051 4,795 5,051
Total 22,575 46,693 20,417 52,230 42,950 53,536
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Nature GeNetics VOLUME 47 | NUMBER 9 | SEPTEMBER 2015 981
A R T I C L E S
SNP rs653178 in a locus harboring SH2B3 and ATXN2 is associated
with multiple other immune-mediated diseases, including celiac dis-
ease and rheumatoid arthritis. It had trans-eQTL effects on 14 genes,
including genes within IBD-associated loci (TAGAP and STAT1).
rs616597 had a cis-eQTL effect on NFKBIZ and had trans-eQTL
effects on FLXB13 (Supplementary Table 6) (ref. 14). Both SNPs
reside in known DNase I hypersensitivity and histone modification
sites in multiple cell lines (Supplementary Table 7). In contrast to
the high number of SNPs tagging eQTLs, only 3 of the 38 SNPs were
in high linkage disequilibrium (LD; r
2
> 0.8) with known missense
coding variants (Supplementary Table 8).
To enable a meaningful comparison with our previously published
results, we recreated the GRAIL connectivity network using all loci
that now achieved genome-wide significant evidence of association
(Supplementary Fig. 8). Twelve genes in the previous GRAIL network
were removed in this new network. We found that these genes had
significantly larger GRAIL P values (Wilcoxon P value = 6 × 10
−4
) and
fewer interaction partners (11.2 versus 16.0) than genes remaining in
the network. Sixty-two genes were connected into the GRAIL network
for the first time, only 36 of which were located within the newly
associated loci (including NFKBIZ, CD28 and OSMR). Thus, 26 genes
from previously established IBD loci are brought into the network
Table 2 Newly associated IBD risk loci
Chr. SNP Position (bp)
Reference
allele
a
Best
phenotype
b
LR
phenotype
c
log
10
(Bayes
factor)
d
Het. (I
2
)
e
European OR European P Candidate gene(s)
1 rs1748195 63,049,593 G CD CD 6.08 0 1.07 (1.04–1.10) 7.13 × 10
−8
USP1
1 rs34856868 92,554,283 A IBD IBD_U 6.16 0 0.82 (0.77–0.88) 9.80 × 10
−9
BTBD8
1 rs11583043 101,466,054 A UC IBD_U 8.34 66.5 1.08 (1.05–1.11) 6.05 × 10
−8
SLC30A, EDG1
1 rs6025 169,519,049 A IBD IBD_U 6.43 0 0.84 (0.79–0.89) 2.51 × 10
−8
SELP, SELE, SELL
1 rs10798069 186,875,459 A CD IBD_S 7.24 0 0.93 (0.91–0.95) 4.25 × 10
−9
PTGS2, PLA2G4A
1 rs7555082 198,598,663 A CD IBD_U 7.97 0 1.13 (1.09–1.17) 1.47 × 10
−10
PTPRC
2 rs11681525 145,492,382 C CD CD 8.8 59.3 0.86 (0.82–0.90) 4.08 × 10
−11
–
2 rs4664304 160,794,008 A IBD IBD_U 6.34 0 1.06 (1.04–1.08) 2.61 × 10
−8
MARCH7, LY75, PLA2R1
2 rs3116494 204,592,021 G UC IBD_S 7.03 0 1.08 (1.05–1.11) 1.30 × 10
−7
ICOS, CD28, CTLA4
2 rs111781203 228,660,112 G IBD IBD_U 10.04 0 0.94 (0.92–0.96) 2.16 × 10
−10
CCL20
2 rs35320439 242,737,341 G CD IBD_S 7.71 0 1.09 (1.06–1.12) 9.89 × 10
−10
PDCD1, ATG4B
3 rs113010081 46,457,412 G UC IBD_U 7.45 0 1.14 (1.09–1.19) 9.02 × 10
−10
FLJ78302, LTF, CCR1,
CCR2, CCR3, CCR5
3 rs616597 101,569,726 A UC UC 6.68 54.7 0.93 (0.90–0.96) 9.34 × 10
−6
NFKBIZ
3 rs724016 141,105,570 G CD CD 7.41 70.9 1.06 (1.04–1.09) 3.36 × 10
−6
–
4 rs2073505 3,444,503 A IBD IBD_U 6.87 0 1.10 (1.06–1.14) 1.46 × 10
−7
HGFAC
4 rs4692386 26,132,361 A IBD IBD_U 6.47 0 0.94 (0.92–0.96) 1.21 × 10
−8
–
4 rs6856616 38,325,036 G IBD IBD_U 9.78 61.6 1.10 (1.06–1.14) 9.72 × 10
−7
–
4 rs2189234 106,075,498 A UC UC 8.85 0 1.08 (1.05–1.11) 1.95 × 10
−10
–
5 rs395157 38,867,732 A IBD IBD_U 19.5 0 1.10 (1.08–1.12) 2.22 × 10
−20
OSMR, FYB, LIFR
5 rs4703855 71,693,899 A IBD IBD_U 6.83 70.3 0.93 (0.91–0.95) 7.16 × 10
−11
–
5 rs564349 172,324,978 G IBD IBD_U 8.12 37.5 1.06 (1.04–1.08) 1.54 × 10
−7
C5orf4, DUSP1
6 rs7773324 382,559 G CD IBD_U 7.67 0 0.92 (0.90–0.94) 1.06 × 10
−9
IRF4, DUSP22
6 rs13204048 3,420,406 G CD IBD_S 7.23 53.5 0.93 (0.91–0.95) 2.89 × 10
−8
–
6 rs7758080 149,577,079 G CD IBD_S 7.88 0 1.08 (1.05–1.11) 7.27 × 10
−9
MAP3K7IP2
7 rs1077773 17,442,679 G UC UC 5.86 76.7 0.93 (0.91–0.95) 5.96 × 10
−9
AHR
7 rs2538470 148,220,448 A IBD IBD_U 10.93 54.6 1.07 (1.05–1.09) 3.00 × 10
−11
CNTNAP2
8 rs17057051 27,227,554 G IBD IBD_U 6.74 15.9 0.94 (0.92–0.96) 5.50 × 10
−8
PTK2B, TRIM35, EPHX2
8 rs7011507 49,129,242 A UC IBD_U 7.49 39.3 0.90 (0.87–0.93) 6.40 × 10
−8
–
10 rs3740415 104,232,716 G IBD IBD_U 6.26 0 0.95 (0.93–0.97) 1.03 × 10
−7
NFKB2, TRIM8, TMEM180
12 rs7954567 6,491,125 A CD CD 8.25 0 1.09 (1.06–1.12) 1.30 × 10
−9
CD27, TNFRSF1A, LTBR
12 rs653178 112,007,756 G IBD IBD_U 6.57 49.7 1.06 (1.04–1.08) 1.11 × 10
−8
SH2B3, ALDH2, ATXN2
12 rs11064881 120,146,925 A IBD IBD_U 7.02 31.7 1.10 (1.06–1.14) 5.95 × 10
−8
PRKAB1
13 rs9525625 43,018,030 A CD CD 8.55 37.3 1.08 (1.05–1.11) 1.41 × 10
−9
AKAP1, TFSF11
17 rs3853824 54,880,993 A CD IBD_S 8.46 50.4 0.92 (0.90–0.94) 1.17 × 10
−10
–
17 rs17736589 76,737,118 G UC UC 6.53 53.4 1.09 (1.06–1.12) 4.34 × 10
−8
–
18 rs9319943 56,879,827 G CD CD 6.33 33.4 1.08 (1.05–1.11) 9.05 × 10
−7
–
18 rs7236492 77,220,616 A CD IBD_S 6.6 0 0.91 (0.88–0.94) 9.09 × 10
−9
NFATC1, TST
22 rs727563 41,867,377 G CD CD 7.1 76 1.10 (1.07–1.13) 1.88 × 10
−10
TEF, NHP2L1, PMM1,
L3MBTL2, CHADL
Loci for IBD, ulcerative colitis or Crohn’s disease were identified through a trans-ancestry analysis of genome-wide and Immunochip genotype data from a cohort of 86,682
European individuals and 9,846 individuals of non-European descent. Loci achieving genome-wide significance (P < 5 × 10
−8
) in one of the individual cohorts of European,
East Asian, Indian or Iranian descent or log
10
(Bayes factor) > 6 in the combined trans-ancestry association analysis were considered to be significantly associated loci. Loci having
log
10
(Bayes factor) > 6 but P > 5 × 10
−8
in each individual ancestral cohort were required to show no significant evidence of heterogeneity across all four ancestry groups
(I
2
> 85.7%). Association P values and odds ratios for the non-European cohorts are given in Supplementary Table 1. Candidate genes were identified by at least one of the
gene prioritization methods we performed (eQTL, GRAIL, DAPPLE and coding SNP annotation (cSNP); see the main text and Online Methods). Genes in bold were prioritized
by >2 gene prioritization strategies. UC, ulcerative colitis; CD, Crohn’s disease; IBD, inflammatory bowel disease; chr., chromosome; OR, odds ratio.
a
The minor allele in the European cohort was chosen to be the reference allele.
b
Phenotype with the largest MANTRA Bayes factor.
c
The preferred phenotype (ulcerative colitis, Crohn’s disease
or IBD) from our likelihood-modeling approach classifying loci according to their relative strength of association. LR, likelihood ratio. IBD_S and IBD_U refer to the IBD saturated and IBD
unsaturated models, respectively (see the main text and Online Methods).
d
MANTRA log
10
(Bayes factor).
e
Heterogeneity I
2
percentage.
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A R T I C L E S
of the T cell response. Thus, beyond the involvement of type 17 helper
T (T
H
17) cells (previously identified through associations with, for
example, IL23R), our results now implicate all three components of
T cell activation (TCR ligation, co-stimulation and interleukin (IL)-2
signaling). Notably, these processes are critical for the development
of immunological memory and are common to both CD4
+
and
CD8
+
T cells.
The functions of leading new positional candidate genes are
discussed in Box 1.
Comparing non-European IBD with European IBD
Recent large-scale trans-ancestry genetic studies of complex diseases
have shown that the majority of risk-associated loci are shared across
divergent populations
8,17,18
. The true extent of sharing is difficult
to characterize because the sizes of non-European cohorts are often
much smaller than their European counterparts, limiting power to
detect associated loci. Despite our study including a large cohort of
9,846 non-European samples and being the largest non-European
study of IBD thus far, this sample size is still small in comparison
with the European cohort of 86,640 individuals. As such, we expect
that the majority of known risk loci will not be associated in the non-
European populations at genome-wide significance. Nevertheless, we
observed a striking positive correlation in the direction of effect when
comparing the 231 independently associated SNPs in the European
and East Asian cohorts (P < 1.0 × 10
−22
for Crohn’s disease and
P < 1.0 × 10
−31
for ulcerative colitis) (Fig. 1). Furthermore, of 3,900
suggestively associated SNPs (5 × 10
−5
≤ P < 5 × 10
−8
) from the
European-only IBD association analysis, 2,566 had the same direction
of effect in the East Asian analysis (P = 5.92 × 10
−88
). Consistent with
for the first time, 12 of which are the only GRAIL gene reported for
the corresponding locus, including TAGAP and IKZF1. Genes within
the 16 previously associated loci that failed to reach genome-wide
significance in our current study had similar average connectivities
as other genes in the network (17.8 versus 16.4, respectively; Wilcoxon
P value = 0.94), thus further supporting their likely involvement in
IBD risk. Thirty-seven of 56 DAPPLE candidate genes were identified
as candidates in the GRAIL analysis (Supplementary Table 9).
Biological implications of newly associated IBD loci
Previous GWAS analyses have highlighted components in several key
pathways underlying IBD susceptibility, many involved in innate immu-
nity, T cell signaling and epithelial barrier function. Accepting the need
for fine mapping to pinpoint causal variants within the newly identified
loci, the current study expands the range of pathways implicated.
The process of autophagy, which is an intracellular process during
which cytoplasmic content is engulfed by double-membrane autophago-
somes and delivered to the vacuole or lysosome for degradation and
recycling, has been implicated in Crohn’s disease pathogenesis since the
identification of ATG16L1 and IRGM as Crohn’s disease susceptibility
genes
15
. The newly identified Crohn’s disease gene ATG4B encodes a
cysteine protease with a central role in this process, reinforcing the
importance of autophagy in Crohn’s disease pathogenesis. Likewise,
the importance of epithelial barrier function in IBD pathogenesis
(previously highlighted by associations with LAMB1 and HNF4A
16
)
is underscored by the new association at OSMR, which modulates a
barrier-protective host response in intestinal inflammation.
Many of the newly identified candidate genes, including LY75,
CD28, CCL20, NFKBIZ, AHR and NFATC1, modulate specific aspects
Box 1 Select candidate genes in the newly associated IBD susceptibility loci
PTGS2 encodes COX-2, an enzyme that converts arachidonic acid into prostaglandins and that is the pharmacological target of non-steroidal anti-inflammatory
drugs (NSAIDs). Prostaglandins were once thought to be exclusively proinflammatory (hence the anti-inflammatory moniker of NSAIDs), although there is now
increasing evidence that some may have important anti-inflammatory roles through inhibiting T cell activation and promoting regulatory T cell development
25
.
Consistent with this new evidence, NSAIDs are generally avoided in IBD, as they are known to precipitate disease flares.
LY75 encodes DEC-205 (also known as CD205), a cell surface receptor that is highly expressed on dendritic cells and is involved in the endocytosis of
extracellular antigens and their presentation on major histocompatibility complex (MHC) class I molecules
26
. This receptor has been shown to have an important
role in T cell function and homeostasis
27
.
CD28 encodes a key co-stimulatory molecule that has an important role in T cell activation. The corresponding locus contains other genes that are also involved
in T cell co-stimulation, including ICOS and CTLA4. Stimulation of T cells in the absence of co-stimulatory signal typically leads to anergy—one of the three
main processes that can bring about tolerance, an important means of preventing aberrant immunological responses to intestinal antigens.
CCL20 encodes a chemokine that is produced by the intestinal epithelium
28
and that binds and activates CCR6. This interaction is important in regulating the
migration of T cells (especially regulatory T cells) and dendritic cells to the gut, with increased production of CCL20 being detectable during inflammation
29
.
Consistent with this evidence, IBD in mouse models is modulated by the absence of CCR6 (ref. 30). The CCR6 locus is itself associated with IBD.
NFKBIZ encodes NF-κB inhibitor ζ (NFKBIZ), an inducible regulator of NF-κB. This protein has been shown to have several functions, including roles in natural
killer cell activation
31
and monocyte recruitment
32
. Recently, however, NFKBIZ has also been shown to be a critical regulator of T
H
17 cell development through
its interaction with ROR nuclear receptors
33
. Accordingly, this association further underlines the importance of T
H
17 cells in IBD pathogenesis.
OSMR encodes the oncostatin M receptor, a cytokine receptor component that heterodimerizes with other proteins to form both the oncostatin M receptor and
the IL-31 receptor. Oncostatin M is present at elevated levels in biopsies from patients with active IBD and is thought to promote intestinal epithelial cell
proliferation and wound healing, thereby augmenting the barrier function of the intestinal epithelium in intestinal inflammation
16
.
AHR encodes the aryl hydrocarbon receptor, a ligand-activated transcription factor that can bind to a range of aromatic hydrocarbons, including several
compounds derived from dietary components. This receptor is highly expressed on T
H
17 cells, and its ligation leads to their expansion and enhanced
production of cytokines, including IL-22 (ref. 34). Moreover, deficiency of this receptor (or its ligands) also disrupts intraepithelial lymphocyte homeostasis,
leading to failure to control intestinal microbial load and composition and aberrant immune activation resulting in epithelial damage
35
. Accordingly, this
association further highlights the importance of the interaction between genes and the environment in IBD pathogenesis.
PTK2B encodes protein tyrosine kinase 2β (also known as Pyk2), an important intracellular kinase for diverse signaling pathways, including mitogen-activated
protein kinase (MAPK) and JNK signaling. Its functions include roles in monocyte migration and neutrophil degranulation.
NFATC1 encodes nuclear factor of activated T cells, cytoplasmic 1, an NFAT transcription factor that is specifically expressed upon activation of T and
B cells following ligation of their respective receptors. This expression supports lymphocyte proliferation and inhibits activation-induced cell death, leading to
enhanced immune responses
36
. NFAT transcription factors are the main molecular targets of calcineurin inhibitors, such as cyclosporine, which are used in the
treatment of IBD.
npg
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