Genome-wide association defines more than 30 distinct susceptibility loci for Crohn's disease.

Abstract

Several risk factors for Crohn's disease have been identified in recent genome-wide association studies. To advance gene discovery further, we combined data from three studies on Crohn's disease (a total of 3,230 cases and 4,829 controls) and carried out replication in 3,664 independent cases with a mixture of population-based and family-based controls. The results strongly confirm 11 previously reported loci and provide genome-wide significant evidence for 21 additional loci, including the regions containing STAT3, JAK2, ICOSLG, CDKAL1 and ITLN1. The expanded molecular understanding of the basis of this disease offers promise for informed therapeutic development.

Full text

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Genome-wide association defines more than 30 distinct
susceptibility loci for Crohn’s disease
Jeffrey C Barrett*1, Sarah Hansoul2, Dan L Nicolae3, Judy H Cho4, Richard H Duerr5,6, John D Rioux7,8,
Steven R Brant9,10, Mark S Silverberg11, Kent D Taylor12, M Michael Barmada6, Alain Bitton13,
Themistocles Dassopoulos9, Lisa Wu Datta9, Todd Green8, Anne M Griffiths14, Emily O Kistner15,
Michael T Murtha4, Miguel D Regueiro5, Jerome I Rotter12, L Philip Schumm15, A Hillary Steinhart11,
Stephan R Targan12, Ramnik J Xavier16, the NIDDK IBD Genetics Consortium33, Ce ´cile Libioulle2,
Cynthia Sandor2, Mark Lathrop17, Jacques Belaiche18, Olivier Dewit19, Ivo Gut17, Simon Heath17,
Debby Laukens20, Myriam Mni2, Paul Rutgeerts21, Andre ´ Van Gossum22, Diana Zelenika17,
Denis Franchimont22, Jean-Pierre Hugot23, Martine de Vos20, Severine Vermeire21, Edouard Louis18,
the Belgian-French IBD Consortium33, the Wellcome Trust Case Control Consortium33,34, Lon R Cardon1,
Carl A Anderson1, Hazel Drummond24, Elaine Nimmo24, Tariq Ahmad25, Natalie J Prescott26,
Clive M Onnie26, Sheila A Fisher26, Jonathan Marchini27, Jilur Ghori28, Suzannah Bumpstead28,
Rhian Gwilliam28, Mark Tremelling29, Panos Deloukas28, John Mansfield30, Derek Jewell31, Jack Satsangi24,
Christopher G Mathew26, Miles Parkes29, Michel Georges2& Mark J Daly8,32
Several risk factors for Crohn’s disease have been identified in recent genome-wide association studies. To advance gene
discovery further, we combined data from three studies on Crohn’s disease (a total of 3,230 cases and 4,829 controls) and carried
out replication in 3,664 independent cases with a mixture of population-based and family-based controls. The results strongly
confirm 11 previously reported loci and provide genome-wide significant evidence for 21 additional loci, including the regions
containing STAT3, JAK2, ICOSLG, CDKAL1 and ITLN1. The expanded molecular understanding of the basis of this disease offers
promise for informed therapeutic development.
Recent genome-wide association studies (GWAS) have identified
many common variants associated with complex diseases and have
rapidly expanded our knowledge of the genetic architecture of these
traits. Progress in Crohn’s disease (CD), a common idiopathic
inflammatory bowel disease (IBD) with high heritability (lsB20–
35), has been especially notable, with recent GWAS publications
increasing the number of confirmed associated loci from two to
more than ten1. The results have identified previously unknown
pathogenic mechanisms of IBD and promise to advance fundamen-
tally our understanding of CD biology. These recent discoveries
highlight, for instance, the importance of autophagy and innate
immunity2–5as determinants of the dysregulated host–bacterial inter-
actions implicated in disease pathogenesis. Furthermore, genetic
associations have been shown to be shared between CD and other
auto-inflammatory conditions—for example, IL23R variants6are also
associated with psoriasis7and ankylosing spondylitis8, and PTPN2
variants with type 1 diabetes3,5. As in other studies of complex
diseases, restricted sample sizes have resulted in early CD studies
focusing on only the strongest effects, which turn out to explain only a
fraction of the heritability of the disease.
We recently published three separate GWA scans for CD in
European-derived populations4,5,9, the details of which are shown in
Table 1. Motivated by the need for larger datasets to improve power to
detect loci of modest effect, we carried out a genome-wide meta-
analysis from our three CD scans. These analyses, together with a
replication study in an equivalently sized independent panel, have
enabled us to identify at genome-wide levels of significance 21 new
CD susceptibility loci. This brings the number of independent loci
conclusively associated with CD to more than 30 and provides
unprecedented insight into both CD pathogenesis as well as the
general genetic architecture of a multifactorial disease.
RESULTS
Meta-analysis of three genome-wide association scans
The combined GWAS study samples (Table 1) consisted of 3,230 CD
cases and 4,829 controls, all of European descent. Although the
Received 1 February; accepted 2 May; published online 29 June 2008; doi:10.1038/ng.175
*A full list of author affiliations appears at the end of this paper.
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individual scans did identify new risk factors, they were only well-
powered to discover common alleles with odds ratios (ORs) above 1.3
(in the case of the WTCCC) or 1.5 (the smaller two scans) (Fig. 1).
By contrast, the combined sample has 74% power at an OR of
1.2, allowing evaluation of the role of alleles with smaller effect sizes
for the first time. As two different genotyping technologies were used
in the constituent scans, we used recently developed imputation10,11
methods to assess association across all three studies at 635,547
SNPs contained on one or both platforms. A quantile-quantile
(Q-Q) plot of the primary meta-statistic (single-SNP Z scores;
Fig. 2) shows a marked excess of significant associations, well beyond
what would be attributable to the modest overall distributional
inflation (genomic control l o 1.16). Despite the large sample size,
the overall inflation is modest because (i) each group had separately
tested for evidence of population stratification, and the meta-analysis
used a test that combined the results from each study (rather than
mixing the raw data and compromising the case-control matching of
each study), and (ii) imputationwas done on all samples ignoring case
status and, thus, would not introduce artifactual differences between
cases and controls12.
We focus our attention in this study specifically on the 526 SNPs
from 74 distinct genomic loci that were associated with P o 5 ? 10?5,
which is more than seven times the number of SNPs expected by
chance even after correction for the modest overall inflation detected.
This threshold for follow-up is not meant to imply that there are no
genuine associations among SNPs with less significant association in
the meta-analysis, but rather reflects a practical desire to prioritize as
many true positives as possible for immediate replication. Eleven
associations previously replicated and established at genome-wide
significance levels (Table 2), including both ‘historical’ associations
at NOD2 (also known as CARD15)13,14and 5q31 (IBD5)15as well as
recent replicated findings from individual GWA scans2–6,16such as
IL23R, ATG16L1, IRGM, TNFSF15 and PTPN2, were among the 74
regions represented in this tail of the distribution of association
statistics. However, even after removal of all SNPs in linkage dis-
equilibrium (LD) with these 11 loci, there continued to be a
substantial excess of associated alleles beyond that which would be
expected by chance (Fig. 2).
Replication of 21 newly identified loci
As these 74 regions included the 11 already reported as independently
replicated and meeting genome-wide significance thresholds, this
replication experiment effectively explored 63 putative associa-
tions in previously unreported regions with 11 positive controls
(Supplementary Table 1 online). To identify the true risk
factors among these 63 regions, we carried out a replication study
involving 2,325 additional CD cases and 1,809 controls alongside an
independent family-based dataset of 1,339 trios of parents and their
affected offspring.
Results (significance levels and ORs) for strongly replicating loci,
including all positive controls, are presented in Table 2. The distribu-
tion of Z scores from the 63 putative regions shows a marked
departure from the null distribution (Fig. 3), with 19 new regions
showing significant replication (P o 0.0008; a value of 0.05/63
representing a conservative threshold expected to be exceeded only
once by chance in 20 such replication experiments). SNPs
on chromosome 19p13 (replication P ¼ 0.00347, combined
P ¼ 2.12 ? 10?9) and in the MHC (replication P ¼ 0.006, combined
P ¼ 5.20 ? 10?9; suspected but not previously conclusively established
in CD) did not reach this conservative threshold, but so convincingly
satisfy proposed thresholds for genome-wide significance (P o 5 ?
10?8) that we propose these as the twentieth and twenty-first addi-
tional CD associated–loci defined here. A further 8 of the 42 remain-
ing loci showed nominal replication (Table 3).
It is possible that extreme population substructure in the replication
sample could give rise to such a marked excess of hits. Although
unlikely, this was directly evaluated by the large family-based compo-
nent of the replication study. Odds ratio estimates from the TDT
analysis of the North American, French and Belgian families alone are
consistent with those from the UK and Belgian case-control samples
(Tables 2 and 3), with all 21 newly defined loci showing ORs in the
same direction of association with the original scan in the family-
based component (and nearly half showing greater OR than in the
case-control arm). None of the significantly or nominally replicating
loci show significant evidence for heterogeneity (across studies or
between family-based and population-based arms) when corrected for
the number of tests performed. This independent family-based evi-
dence confirms that these alleles constitute true CD-associated loci.
For this newly expanded set of 32 unequivocally associated loci, we
assessed whether there was evidence of significant pairwise interac-
tions that could add further to the overall variance in liability
explained by this set of loci. We carried out a case-only analysis of
the 3,664 cases in the replication study and observed no interactions
that withstood a correction for the number of tests performed
(Supplementary Table 2 online).
Table 1 Samples used (post quality control) in this study
NIDDKBEL-FRUKTotal
Scan cases
Scan controls
Replication cases
Replication controls
Replication trios
Nationality
Scan platform
946
977
536
914
1,082
787
619
1,748
2,938
1,243
1,022
3,230
4,829
2,325
1,809
1,339
0
0
7200
US-Canadian
Illumina
HumanHap300
Sequenom
Belgian-French
Illumina
HumanHap300
Illumina GoldenGate
British
Affymetrix
GeneChip 500K
SequenomReplication platform
Scans, by sample size
Power
0.0
0.2
0.4
0.6
0.8
1.0
BE-FR
NIDDK
WTCCCMETA
OR = 1.5
OR = 1.3
OR = 1.2
Figure 1 Power to detect a genetic effect of various sizes (odds ratio
1.2, 1.3 or 1.5) versus study sample size. Power is reported here as the
probability (given a multiplicative model and risk allele frequency of 20%)
of P o 5 ? 10?5in a scan, which was the value used to define regions for
attempting replication in a larger sample set. Vertical dotted lines show the
sample sizes for the three constituent scans and the meta-analysis. Relatively
large effects are likely to be detected by any of these scans, whereas only the
combined analysis is well powered to detect more modest effects.
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Deciphering the genetic architecture of CD
The contributions of the 32 loci to disease risk were computed using a
standard liability threshold model and are displayed as a histogram of
individual variances (Fig. 4). The observations from this variance
analysis indicate that many loci were detected for which the current
study had low power and that only a minority of the variance in risk is
explained by these 32 loci, suggesting that many additional loci remain
to be identified. This is reinforced by the additional eight nominal
replications (Table 3), where only two or three would be expected by
chance, and by the continued excess of small P values when these 40
total regions are removed (Fig. 2).
Although we recognize that fine-mapping is required to identify
specific causal variants, we carried out a series of analyses to gain some
general insight into the CD associations. We first queried HapMap to
discover any instances where a nonsynonymous SNP (nsSNP) was
correlated (r24 0.5) to the most associated variant discovered in this
study. Accepting that HapMap is not a complete catalog of nsSNPs, but
0
Expected
Observed
0
2
4
6
8
654321
Figure 2 A quantile-quantile plot of observed –log10P values versus the
expectation under the null. Black points represent the complete meta-
analysis, with a substantial departure from the null at the tail (values 4 8
are represented along the top of the plot as triangles). Dark blue points show
the distribution after removal of 11 previously published loci, demonstrating
a still notable excess. Light blue points show the distribution after removal
of all 40 loci that replicate at least nominally. In all the cases, the overall
distribution is marginally inflated (lGCo 1.16).
Table 2 Convincingly (Bonferroni P o 0.05) replicated CD risk loci
P valuesOdds ratios
SNPChr.Critical regionScanReplicationCombinedNo. genesGene of interestRAFRisk alleleCase CtrlTDT
Previously published loci
rs11465804
rs3828309
rs3197999
rs4613763
rs2188962
rs11747270
rs4263839
rs10995271
rs11190140
rs2066847
rs2542151
1p31
2q37
3p21
5p13
5q31
5q33
9q32
10q21
10q24
16q12
18p11
67.4a
230.9a
48.73–49.87
40.32–40.48
131.44–131.90
150.15–150.32
114.61–114.78
64.05–64.12
101.26–101.32
49.3a
12.73–12.88
1.01 ? 10?35
1.13 ? 10?20
2.16 ? 10?7
4.52 ? 10?22
4.58 ? 10?9
6.36 ? 10?11
3.92 ? 10?7
1.90 ? 10?11
1.71 ? 10?10
n.a.
1.19 ? 10?11
3.10 ? 10?29
7.67 ? 10?14
5.64 ? 10?7
2.79 ? 10?8
3.52 ? 10?11
2.57 ? 10?7
6.58 ? 10?5
1.61 ? 10?10
1.69 ? 10?7
1.49 ? 10?24
2.41 ? 10?7
6.66 ? 10?63
2.36 ? 10?32
1.15 ? 10?12
6.82 ? 10?27
2.32 ? 10?18
3.40 ? 10?16
2.60 ? 10?10
4.46 ? 10?20
3.06 ? 10?16
2.98 ? 10?24
5.10 ? 10?17
n.a.
n.a.
35
0
7
3
2
1
1
n.a.
1
IL23R
ATG16L1
MST1b
PTGER4c
0.933
0.533
0.271
0.125
0.425
0.090
0.677
0.387
0.478
0.018
0.152
T
G
A
C
T
G
G
C
T
C
G
2.50
1.28
1.20
1.32
1.25
1.33
1.22
1.25
1.20
3.99
1.35
2.77
1.30
1.20
1.28
1.26
1.31
1.07
1.53
1.28
2.57
1.14
IRGM
TNFSF15
ZNF365
NKX2-3
NOD2
PTPN2
Newly identified loci
rs2476601
rs2274910
rs9286879
rs11584383
rs10045431
rs6908425
rs7746082
rs2301436
rs1456893
rs1551398
rs10758669
rs17582416
rs7927894
rs11175593
rs3764147
rs2872507
rs744166
rs1736135
rs762421
1p13
1q23
1q24
1q32
5q33
6p22
6q21
6q27
7p12
8q24
9p24
10p11
11q13
12q12
13q14
17q21
17q21
21q21
21q22
113.79–114.17
157.65–157.72
169.54–169.67
197.60–197.77
158.69–158.76
20.63–20.84
106.52–106.62
167.32–167.52
50.03–50.11
126.60–126.62
4.94–5.26
35.30–35.60
75.80–76.02
38.61–39.31
43.13–43.54
34.63–35.34
37.74–37.95
15.73–15.76
44.43–44.48
1.81 ? 10?5
3.50 ? 10?7
4.02 ? 10?7
6.82 ? 10?7
8.80 ? 10?9
2.52 ? 10?7
3.70 ? 10?6
3.30 ? 10?7
4.92 ? 10?5
4.90 ? 10?6
6.80 ? 10?7
8.48 ? 10?6
1.43 ? 10?7
1.33 ? 10?7
1.61 ? 10?7
2.12 ? 10?6
5.94 ? 10?6
2.06 ? 10?5
1.08 ? 10?5
0.000101
0.000481
0.000321
2.34 ? 10?6
3.66 ? 10?6
0.000278
7.70 ? 10?6
3.26 ? 10?7
1.10 ? 10?5
0.000109
0.00043
2.53 ? 10?5
0.000732
0.000165
1.33 ? 10?7
0.000292
9.15 ? 10?8
4.58 ? 10?5
1.59 ? 10?5
1.46 ? 10?8
1.46 ? 10?9
1.53 ? 10?9
1.43 ? 10?11
3.86 ? 10?13
8.96 ? 10?10
2.44 ? 10?10
1.04 ? 10?12
4.60 ? 10?9
4.50 ? 10?9
3.46 ? 10?9
1.79 ? 10?9
1.32 ? 10?9
3.08 ? 10?10
2.08 ? 10?13
5.00 ? 10?9
6.82 ? 10?12
7.40 ? 10?9
1.41 ? 10?9
7
2
0
3
1
1
0
3
0
0
3
3
1
3
3
17
4
0
1
PTPN22
ITLN1
0.899
0.682
0.243
0.697
0.708
0.780
0.289
0.463
0.678
0.619
0.348
0.345
0.386
0.017
0.221
0.473
0.565
0.565
0.389
G
C
G
T
C
C
C
T
A
A
C
G
T
T
G
A
A
T
G
1.31
1.14
1.19
1.18
1.11
1.21
1.17
1.21
1.20
1.08
1.12
1.16
1.16
1.54
1.25
1.12
1.18
1.18
1.13
1.17
1.62
1.08
1.20
1.36
1.09
1.19
1.16
1.14
1.25
1.21
1.26
1.07
1.44
1.19
1.24
1.25
1.10
1.21
IL12B
CDKAL1
CCR6
JAK2
C11orf30
LRRK2,MUC19
ORMDL3
STAT3
ICOSLG
RAF is risk allele frequency in control samples (see Supplementary Table 5 online for details). Critical region is in NCBI Build 35 coordinates, with definition as described in
Methods. Risk alleles are defined relative to the +strand of the reference. P values for the TDT analysis for these loci are listed in Supplementary Table 6 online.
aRegions where causal variants have been convincingly mapped, rendering the LD window uninformative.bRecently implicated through work reported in ref. 28.cPTGER4 is outside
the critical region, but was implicated via eQTL analysis.
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including four loci where fine mapping has identified coding variants,
we found that just 9 of the 32 genome-wide significant associations
were correlated with a known nsSNP (Supplementary Table 3 online).
To explore whether any of the associations reflect a cis-acting regulatory
effect on a nearby gene, we evaluated genotype–expression correlation
using the panel of 400 lymphoblastoid cell lines previously described17.
From all genes within 250 kb of the LD-based intervals defined in
Tables 2 and 3, we identified five correlations between expression of a
nearby gene and a CD-associated variant (LOD 4 2) (Supplementary
Table 4 online). This was far in excess of chance (P B0.001)
(Supplementary Fig. 1 online) and suggests that regulatory variation
also contributes to the genetic architecture identified.
DISCUSSION
Genome-wide association studies provide a systematic assessment of
the contribution of common variation to disease pathogenesis.
A limiting factor is often the size of the case-control dataset, and
hence the power to detect any but the most strongly associated loci.
Meta-analysis of existing data provides an obvious potential solution.
As Figure 1 shows, our expectation was that the additional power of
the combined dataset would result in the identification of a substan-
tially larger number of readily replicating associations than were
derived from any of the smaller, constituent datasets. However, the
paradigm of exploring common genetic variation with similar effects
across studies (in this case all of European descent) needs to be tested
before its results can be accepted as valid.
On the validity of the method, our results are substantially
reassuring. All 11 previously confirmed CD susceptibility loci were
strongly replicated both in the meta-analysis and follow-up experi-
ment. These include the two widely replicated findings from studies
published in 2001 (refs. 13–15) as well as all of the compelling findings
from individual GWAS (Table 2). We also identified and replicated
21 new CD susceptibility loci. Using a conservative threshold for
significance (only one such region would be expected by chance in 20
such experiments), the loci with clear evidence for association in the
replication panel include a very high proportion of those showing the
strongest signals in the meta-analysis (Supplementary Table 1)—9 of 9
previously unreported regions with P o 5 ? 10?7in the combined
scan were replicated convincingly—emphasizing the validity of the
meta-analysis results. Further, all 21 of these loci exceed a conservative
genome-wide level of significance (P o 5 ? 10?8) by a substantial
margin (all but two have P o 5 ? 10?9), and equivalent strength
of association was observed in the family-based subset of our
replication sample.
In keeping with other regions recently identified as associated with
CD, the 21 newly identified loci do not conform to any obvious
pattern in terms of gene content. Thus, as shown in Table 2, some loci
(defined by HapMap recombination hotspots flanking the set of
correlated, associated variants) contain just a single gene, some
contain many genes and others none. Clearly, the first category
provides the most immediate clues regarding pathogenic mechanisms.
These genes are discussed briefly in Box 1, together with a number of
genes that constitute noteworthy candidates from regions with only a
handful of transcripts. Included among these are compelling func-
tional candidates such as STAT3, JAK2 and IL12B, whereas others,
such as CDKAL1 and PTPN22, highlight potentially intriguing con-
trasts between genetic susceptibility to CD and other complex
disorders (Box 1). Consistent with previous findings from CD and
other complex diseases, we did not find any strong evidence of
Z scores
Density
0–2–4 –66
0.0
0.1
0.2
0.3
0.4
0.5
42
Figure 3 Distribution of observed Z scores from the 63 newly identified
regions explored, along with the expected distribution under the null
(a standard normal with mean 0 and variance 1). Even when the 21 regions
reaching genome-wide significance are set aside, the distribution is highly
skewed: four more results exceed a Z of 2 (one would be expected by
chance under the null) and none showed a Z of less than –2 (same
expectation under the null), suggesting that even more of the regions
investigated here are likely to be found to constitute true-positive
associations when additional data become available.
Table 3 Nominally (uncorrected P o 0.05) replicated CD risk loci
P valuesOdds ratios
SNPChrCritical regionScanReplicationCombinedNo. genesGene of interestRAFRisk allele CaseCtrl TDT
rs4807569
rs780094
rs3763313
rs13003464 2p16
rs991804
rs12529198 6p25
rs17309827 6p25
rs7758080
rs8098673
rs917997
19p13
2p23
6p21
1.05–1.15
27.30–27.77
32.44–32.79a1.45 ? 10?8
61.09–61.14
29.57–29.70
5.04–5.11
3.36–3.42
6q25 149.54–149.65 7.28 ? 10?6
18q1117.74–17.93
2q11 102.31–102.64 2.16 ? 10?5
RAF is risk allele frequency in control samples (see Supplementary Table 5 for details). Critical region is in NCBI Build 35 coordinates, with definition as described in Methods. Risk
alleles are defined relative to the +strand of the reference.
aSNPs with P o 0.0001 were observed throughout the MHC from 30.2–32.9 Mb, but only this largest signal from the region was followed up. More detailed study of the MHC will
be required to identify and localize potentially independent signals from this region.
1.16 ? 10?8
3.82 ? 10?6
0.00347
0.00381
0.00602
0.00565
0.0135
0.0192
0.0391
0.044
0.0443
0.0493
2.12 ? 10?9
3.14 ? 10?7
5.20 ? 10?9
4.60 ? 10?6
1.07 ? 10?6
6.96 ? 10?7
2.74 ? 10?6
8.78 ? 10?6
2.88 ? 10?5
2.22 ? 10?5
2
22
7
1
4
1
1
0
0
5
0.217
0.397
C
T
C
G
C
G
T
G
C
T
1.02
1.08
1.19
1.16
1.10
1.12
1.10
1.12
1.05
1.05
1.26
1.13
1.01
1.08
1.08
1.19
1.02
0.99
1.09
1.11
GCKR
BTNL2, SLC26A3, HLA-DRB1, HLA-DQA1 0.188
PUS10
CCL2, CCL7
LYRM4
SLC22A23
3.44 ? 10?5
4.02 ? 10?6
7.08 ? 10?7
2.08 ? 10?6
0.376
0.726
0.062
0.639
0.274
0.329
0.222
17q12
3.18 ? 10?5
IL18RAP
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deviation from the model of multiplicative (random) effects when we
tested for gene–gene interactions among the 32 confirmed associa-
tions, in spite of the fact that some of these genes seem to affect the
same or overlapping pathways.
For loci containing multiple genes or no genes, the picture is less
well defined. The identified paucity of correlation between associated
SNPs and coding variation suggests that these loci in particular may
benefit from eQTL (expression quantitative trait locus) analysis. This
approach seeks to correlate genotype and expression patterns and
takes into account that such functional relationships need not respect
the specific boundaries of LD around the association. One of our
groups previously reported an eQTL effect implicating PTGER4 at the
5p13 locus9. A notable outcome from our present analysis was at the
established IBD5 locus15, where CD-associated SNPs were associated
with decreased SLC22A5 mRNA expression. Although a SNP had
previously been proposed as regulating SLC22A5 transcriptional
activity18, these data suggest for the first time that the most disease-
associated variants in the IBD5 region, including a coding variant in
neighboring SLC22A4, are the same variants most associated with
SLC22A5 expression. Further, the most significant CD-associated
eQTL reported here affects ORMDL3 (lod score ¼ 20) on chromo-
some 17, and SNPs in precisely the same region were recently shown
to be strongly associated with childhood asthma19. This suggests that
the same polymorphisms might underlie susceptibility to both CD
and asthma, possibly by perturbing ORMDL3 expression.
The new loci that we identified are of modest effect size, which is
unsurprising given that all loci with larger impact on disease risk were
discovered in the original scans (as might be expected). The small sizes
of these effects explains the lack of overlap between linkage results in
CD and these newly discovered loci (Supplementary Fig. 2 online),
with the possible exceptions of combined effects of multiple high-
ranking associations on chromosomes 5q and 6p. Indeed, the linkage
evidence that led to the discovery of the IBD5 locus was very likely
boosted by the nearby effects at IL12B and IRGM. As expected, the
Variance explained (%)
Number of loci
0
2
4
6
8
10
12
0
0.25
0.5
0.75
1
00.2 0.40.60.8>1
Power
IL23R, NOD2
Figure 4 Histogram of percent variance explained by each of the 32
established CD risk loci. The distribution resembles the long postulated
exponential distribution of effect sizes. Dashed line shows the joint power for
our meta-analysis to detect (P o 5 ? 10?5), and for our replication sample
to replicate (at Bonferroni corrected P values), a 20% variant explaining a
given fraction of variance. Note how quickly this curve moves from nearly
zero power to detect tiny effects (less than one tenth of one percent) to
nearly full power to detect larger effects (presuming they are well covered by
the current generation of GWAS chips). Complete power near the origin would
likely reveal a more complete exponential distribution, with many very small
effects. These are likely to increase somewhat once the causal variant or
variants are identified in each locus. Indeed, NOD2 and IL23R are distant
outliers, each explaining 1–2% of total variance, partially because multiple
causal variants have already been discovered at these loci6,13.
Box 1 Noteworthy genes within loci newly implicated in Crohn’s
disease pathogenesis
? CCR6 (chemokine receptor 6): encoding a member of the G protein–coupled
chemokine receptor family, this homing receptor is expressed by immature dendritic
cells and memory Tcells and is important for B-cell differentiation and tissue-specific
migration of dendritic and T cells during epithelial inflammatory and immunological
responses29. The ligand of this receptor is macrophage inflammatory protein 3 a
(MIP-3a); both genes are expressed in granulomas of pulmonary sarcoid30. Recent
studies have also demonstrated that CCR6, IL23R and RORC are selectively expressed
by IL-17 producing cells and IFNg-producing Th17 and Th1 cells in CD31.
? IL12B: this gene encodes the p40 subunit, which is a constituent of both
heterodimeric interleukins IL-12 and IL-23 (ref. 32). Its association with CD has been
previously reported5but not confirmed, and it is also known to be associated with
psoriasis7. The key role of the IL-12–IL-23 pathway in chronic intestinal inflammation
is supported by the association between IL23R and CD3and strong functional
evidence from mouse models of colitis33–36.
? STAT3 (signal transducer and activator of transcription 3) and JAK2 (Janus kinase
2): the JAK-STAT pathway is a focal point in signal transmission downstream of
cytokine and growth factor signals from cell surface receptors to the nucleus to modify
transcription of various genes, notably in hematopoietic cells. The present findings are
particularly significant, given the role of both genes in IL23R signaling37and the
central role of STAT3 in Th17 differentiation38. However, JAK2 and STAT3 are also
downstream of several other cytokines implicated in CD pathogenesis in addition to
IL-23, highlighting the pathophysiologic complexity of these new associations. Further
complexity is highlighted by the distinctly different roles of STAT3 in innate versus
adaptive immunity in mouse colitis models: activation of STAT3 in innate immune cells
enhances mucosal barrier function, whereas STAT3 activation in T cells exacerbates
colitis.
? LRRK2 (leucine-rich repeat kinase 2): this gene encodes a multi-domain protein
expressed mainly in the cytoplasm of neurons, myeloid cells and monocytes, and
mutations in LRRK2 have been strongly associated with Parkinson’s disease39.
A recent study39reported the induction of autophagy by mutant LRRK2, which is of
interest given the strong associations between CD and the autophagy genes ATG16L1
and IRGM2–5. The same locus also contains the gene MUC19, which encodes a large
protein with multiple serine- and threonine-rich repeats characteristic of the proteins
encoded by the mucin gene family. The mucin proteins are core components of the
mucus layer, which protects the intestinal epithelium from injury, and mucin defi-
ciency leads to intestinal inflammation in mouse models of colitis40.
? CDKAL1: the protein encoded by this gene is poorly characterized, but CDKAL1 is
noteworthy for being recently confirmed as gene associated with type 2 diabetes
susceptibility24,41–43. In this study, we find that SNPs from the same intron of
CDKAL1 that shows association with T2D are associated with CD, but the associated
alleles for the two diseases are not correlated with each other.
? ICOSLG (inducible T-cell co-stimulator ligand): this co-stimulatory molecule is
expressed on intestinal (and other) epithelial cells and may have a role in their antigen
presentation to and regulation of mucosal T lymphocytes44. Upon maturation,
plasmacytoid dendritic cells express ICOSLG and drive the generation of
IL-10–producing regulatory T cells45.
? PTPN2 and PTPN22 (protein tyrosine phosphatase, nonreceptor types 2 and 22):
both of these genes are associated with other autoimmune and inflammatory diseases
and the effect described here for PTPN2 is similar to that previously described for type
1 diabetes (T1D)46. However, the association of PTPN22 with CD, although mapping
to the same coding variant (R602W) that is a risk factor for T1D and rheumatoid
arthritis47,48, is in the opposite direction, with the T1D and rheumatoid arthritis risk
allele, 602W, offering protection from CD.
? ITLN1 (intelectin-1): this gene known to be expressed in human small bowel and
colon, and encodes a 120-kDa homotrimeric lectin recognizing galactofuranosyl
residues found in cell walls of various microorganisms but not in mammals49. Human
intelectin-1 is structurally identical to the lactoferrin receptor (LFR), expressed within
the enterocyte brush border, and seems critical in membrane stabilization, preventing
loss of digestive enzymes and protecting the glycolipid microdomains from patho-
gens50. In addition, intelectin expression is reported in Paneth cells in both mouse and
pig small intestine, further pointing to a role in innate immunity.
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only gene conclusively discovered via linkage (NOD2) is one of two
loci which stand well out from the remainder of the distribution of
effect sizes (Fig. 4). The other outlier, IL23R, illustrates an interesting
characteristic of linkage: because (unlike in NOD2) the most penetrant
risk allele has very high frequency (93%), it is nearly invisible to
linkage analysis despite the high OR; highly protective rare alleles are
simply not present in multiplex affected families and thus do not
influence allele sharing substantially.
Using a liability-threshold model, we estimate that the 32 loci
identified to date explain about 10% of the overall variance in disease
risk, which may be as much as a fifth of the genetic risk, given previous
estimates of CD heritability of approximately 50% (ref. 20). This
observation is consistent with the fact that these loci collectively
contribute only a factor of two to sibling relative risk (ls), and even
this figure is dominated by the substantial contribution of NOD2
variants. However, it should be emphasized that the full impact of the
new loci cannot be determined until causal variants have been
identified by directed sequencing and fine-mapping experiments.
Until then, the proportion of the variance in CD risk explained
must be measured from the confirmed SNPs, where association is
due to LD with causal variants. As multiple causal variants might exist
at each locus (ranging in frequency from rare to common), our
estimates of variance explained provide only a lower bound for the
true contribution of each locus.
In conjunction with results from a very similar gene discovery effort
in type 2 diabetes21, common lessons are beginning to emerge with
respect to the genetic architecture of complex traits. In each example, a
substantial increase in sample size achieved through meta-analysis has
led to dramatic improvements in gene discovery. In all cases, this
progress has revealed an underlying architecture consistent with many
individually modest effects, which conventional genetic linkage
analysis, and even the largest individual genome-wide association
studies, are not well powered to detect. Common variants explaining
more than 1% of the genetic variance are rare, whereas well-powered
studies have found dozens of variants contributing 0.1% of overall
variance in liability. Perhaps surprisingly, neither we nor others
have yet to document a substantial role for epistasis among these
loci, and a number of associated loci are conclusively mapped
to regions with no currently annotated protein-coding genes.
Despite the considerable concordant success, a distinct minority
of the overall heritability has been explained by these docu-
mented associations.
As our study is well powered to identify loci that explain 4 0.2% of
the overall variance, but the sum of such loci explains a relatively small
fraction of the total, it seems likely that many loci with even more
modest effect sizes remain undiscovered. Of particular note is the
continued excess of associations outside of the regions studied here, as
well as the nominal replication of an additional eight loci, notably
greater than expected by chance. Overall, the distribution of Z scores in
the replication experiment is clearly skewed toward replication: only 11
of the 63 Z scores in this replication experiment generate Z o 0. If only
the 21 strongly confirmed loci were genuinely associated, half of the 42
remaining should end up with Z o 0. Indeed, that 8 of the
42 remaining tests have Z 4 1.5 is itself a highly significant observation
(P o 0.0001). Although modest in terms of effect size, identification of
such loci is likely to still provide important insights into pathogenic
mechanisms, as biological importance need not be proportional to the
statistical evidence for genetic association. Closer inspection of regions
showing nominal association in the replication experiment reveals that
a number of transcripts in these loci are of considerable interest,
including CCL2 and CCL7 (ref. 22), IL18RAP23and GCKR24.
It is important to note that the GWAS arrays used for these scans did
not offer complete genome coverage of common variation (additional
loci may reside in poorly covered intervals) and did not address either
rare SNPs or copy number variation effectively. Thus, in spite of the
wealth of new susceptibility lociidentified by the current study, it seems
plausible that there are still more to be found; however, very large
datasets are likely to be required to achieve robust statistical support for
them. With respect to the present findings, there is much work to be
done in resequencing and fine mapping to identify causal variants.
Although we do not yet have a complete understanding of the genetic
architecture of CD, dramatic progress has now been made toward this
goal—and with it, the prospect of directed functional exploration of
the pathways identified, insight into how risk alleles interact with
environmental modifiers and the hope of new avenues for treatment.
METHODS
Subjects and GWAS. The meta-analysis was based on data from the three
genome-wide scans of the NIDDK4, WTCCC5and Belgian-French9studies.
Details of the numbers of cases and controls genotyped in the respective scans
and of the genotyping platforms used are shown in Table 1, as are details of the
case-control and family cohorts genotyped in the replication study of the meta-
analysis. Details of the ascertainment and characterization of these cohorts, as
well as of quality control procedures applied to the GWA datasets, were
provided in the original scan and replication publications3–6,9. Recruitment
of study subjects was approved by local and national institutional review
boards, and informed consent was obtained from all participants.
Imputation. These methods rely on observed haplotype patterns in a set of
reference data (the HapMap) and the actual genotype data from each project to
make predictions (along with a measure of statistical certainty) at ungenotyped
SNPs. We used the program MACH10with the NIDDK and Belgian-French
data, and IMPUTE11with the WTCCC data. Comparisons between the two
algorithms yielded very similar results (data not shown). We imputed the
superset of polymorphic markers that passed quality control in the original
scans4,5,9. This set was comprised of SNPs on either the Affymetrix 500K only
(n ¼ 350,507), Illumina HumanHap300 version 1 only (n ¼ 238,935) or both
panels (n ¼ 46,105) such that all association tests done were at least partially
based on observed genotype data.
Test for association, effect size estimation and interactions. Using the
genotype probabilities (rather than best-guess genotypes) and empirical var-
iances for imputed markers in the case and control tallies, we summarized the
standard 1 d.f. allele-based test of association as a Z score within each scan and
combined scores across studies to produce a single meta-statistic for each SNP
across all three datasets. Odds ratios were estimated separately in TDTsamples
and each case-control replication collection and then combined and tested for
heterogeneity25. Interaction tests were done using the case-only epistasis test
implemented in PLINK26.
Critical regions. Given that most associations contain many correlated SNPs
showing signal, we demarcated independent loci by first defining the set of
HapMap SNPs with r24 0.5 to the most significantly associated SNP. We then
bounded the ‘critical region’ by the flanking HapMap recombination hotspots
that contained this set. These windows very likely contain the causal poly-
morphisms explaining the associations.
Replication. We defined loci to have been previously confirmed if an earlier
study had both detected and replicated the association in independent samples
and the association achieved P o 5 ? 10?8(recently proposed as an
appropriate genome-wide significance level for GWAS27). For replication
genotyping, we selected the most significantly associated SNP from each region
along with a second, correlated SNP with P o 0.0001 or a second assay on the
opposite strand in order to have a technical backup should the first fail
genotyping (Supplementary Table 1). Replication genotyping for the
putatively associated loci was done using primer extension chemistry and mass
spectrometric analysis (iPLEX, Sequenom) using Sequenom Genetics Services
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(North American panel) and Genome Research Limited, Wellcome Trust
Sanger Institute (UK panel), and using a custom-made Golden Gate assay
on a Beadstation500 (Illumina), following the manufacturer’s recommenda-
tions (Belgian-French panel). The more completely genotyped SNP of the two
from each region was chosen to represent that regional association in
analysis (if both were completely typed, the SNP that was more strongly
associated in the scan was used). Samples with 410% missing data (n ¼ 267
for Belgian-French data, 111 for the UK data and 8 for the North American
data; these samples are not included in the tallies for Table 1), as well as SNPs
with 410% missing data or Hardy-Weinberg P value o0.001, were excluded
from this analysis.
Regional annotation via eQTL analysis. The effects of SNPs listed in Tables 2
and 3 on expression of neighboring genes were studied using transcriptome
data from the B400 lymphoblastoid cell lines described previously17. SNPs that
were not genotyped on this panel (n ¼ 14) were replaced with a proxy with
r24 0.95 when possible (n ¼ 12). Lod scores 4 2 for genes (probe average)
located within 250 kb of the corresponding LD windows were retrieved (see
URLs section below). To evaluate the significance of the findings with the
CD-associated SNPs, we compared the observed (i) number of genes yielding
lod scores 4 2, and (ii) sum of these lod scores, with the corresponding
frequency distributions for 1,000 randomly selected sets of 31 SNPs, matched
for allele frequency (±0.02) and gene context. Window sizes determined for
associated SNPs were used for the matched simulated SNPs.
URLs. mRNA by SNP Browser, http://www.sph.umich.edu/csg/liang/asthma/;
meta-analysis test statistics and allele frequencies for all SNPs, http://
www.broad.mit.edu/~jcbarret/ibd-meta/.
Note: Supplementary information is available on the Nature Genetics website.
ACKNOWLEDGMENTS
We acknowledge use of DNA from the 1958 British Birth Cohort collection
(R. Jones, S. Ring, W. McArdle and M. Pembrey), funded by the Medical
Research Council (grant G0000934) and The Wellcome Trust (grant 068545/Z/
02), and the UK Blood Services Collection of Common Controls (W. Ouwehand)
funded by the Wellcome Trust. We also acknowledge the National Association for
Colitis and Crohn’s disease and the Wellcome Trust for supporting the case DNA
collections, and support from UCB Pharma (unrestricted educational grant) and
the NIHR Cambridge Biomedical Research Centre. The National Institute of
Diabetes and Digestive and Kidney Disease (NIDDK) IBD Genetics Consortium
is funded by the following grants: DK62431 (S.R.B.), DK62422 (J.H.C.), DK62420
(R.H.D.), DK62432 and DK064869 (J.D.R.), DK62423 (M.S.S.), DK62413
(K.D.T.), NIH-AI06277 (R.J.X.) and DK62429 (J.H.C.). Additional support was
provided by the Burroughs Wellcome Foundation (J.H.C.) and the Crohn’s and
Colitis Foundation of America (S.R.B., J.H.C.). We thank P. Gregersen and A. Lee
(Feinstein Medical Research Institute) for their efforts and the use of control
samples. This work was supported by grants from the DGTRE from the Walloon
Region (n1315422 and CIBLES), the Communaute ´ Franc ¸aise de Belgique
(Biomod ARC), and the Belgian Science Policy organisation (SSTC Genefunc and
Biomagnet PAI). E.L., S.Hansoul., D.F. and S.V. are fellows of the Belgian Fonds
de la Recherche Scientifique (FNRS) and Fonds Wetenschappelijk Onderzoek-
Vlaanderen (NFWO). C.S. is a fellow of the FRIA. We are grateful to all the
clinicians, consultants and nursing staff who recruited subjects, including:
J.-M. Maisin, V. Muls, J. Van Cauter, M. Van Gossum, P. Closset, P. Hayard
and J.M. Ghilain (Erasme-BBIH-IBD); P. Mainguet, F. Mokaddem, F. Fontaine,
J. Deflandre and H. Demolin (Ulg collaborators); J.-F. Colombel, M. Lemann,
S. Almer, C. Tysk, Y. Finkel, M. Gassul, C. O’Morain, V. Binder and J.-P. Ce ´zard
(INSERM collaborators). Sincere thanks to L. Liang for his assistance in accessing
the eQTL database, and to F. Merlin for expert technical assistance. Finally, we
thank all individuals who contributed samples.
Published online at http://www.nature.com/naturegenetics/
Reprints and permissions information is available online at http://npg.nature.com/
reprintsandpermissions/
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1Bioinformatics and Statistical Genetics, Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK.2Unit of Animal
Genomics, GIGA-R and Faculty of Veterinary Medicine, University of Lie `ge, Belgium.3University of Chicago, Department of Medicine, 5801 South Ellis, Chicago,
Illinois 60637, USA.4Yale University, Departments of Medicine and Genetics, Division of Gastroenterology, Inflammatory Bowel Disease (IBD) Center, 300 Cedar
Street, New Haven, Connecticut 06519, USA.5University of Pittsburgh, School of Medicine, Department of Medicine, Division of Gastroenterology, Hepatology and
Nutrition, University of Pittsburgh Medical Center (UPMC) Presbyterian, 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, USA.6University of Pittsburgh,
Graduate School of Public Health, Department of Human Genetics, 130 Desoto Street, Pittsburgh, Pennsylvania 15261, USA.7Universite ´ de Montre ´al and the
Montreal Heart Institute, Research Center, 5000 rue Belanger, Montreal, Quebec H1T 1C8, Canada.8The Broad Institute of Massachusetts Institute of Technology and
Harvard, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA.9Johns Hopkins University, Department of Medicine, Harvey M. and Lyn P. Meyerhoff
Inflammatory Bowel Disease Center, 1503 East Jefferson Street, Baltimore, Maryland 21231, USA.10Johns Hopkins University, Bloomberg School of Public Health,
Department of Epidemiology, 615 E. Wolfe Street, Baltimore, Maryland 21205, USA.11Mount Sinai Hospital IBD Centre, University of Toronto, 441-600 University
Avenue, Toronto, Ontario M5G 1X5, Canada.12Medical Genetics Institute and Inflammatory Bowel Disease (IBD) Center, Cedars-Sinai Medical Center, 8700 W. Beverly
Blvd., Los Angeles, California 90048, USA.13Department of Medicine, Royal Victoria Hospital, McGill University, Montreal, Quebec, H3A 1A1, Canada.14The Hospital
for Sick Children, University of Toronto, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.15University of Chicago, Department of Health Studies, 5841
S. Maryland Avenue, Chicago, Illinois 60637, USA.16Gastrointestinal Unit and Center for Computational and Integrative Biology, Massachusetts General Hospital,
Harvard Medical School, 185 Cambridge Street, Boston, Massachusetts 02114, USA.17Centre National de Ge ´notypage, Evry, France.18Unit of Hepatology and
Gastroenterology, Department of Clinical Sciences, GIGA-R, Faculty of Medicine and CHU de Lie `ge, University of Lie `ge, Belgium.19Department of Gastroenterology,
Clinique universitaire St Luc, UCL, Brussels, Belgium.20Department of Hepatology and Gastroenterology, Ghent University Hospital, Belgium.21Department of
Gastroenterology, University Hospital Leuven, Belgium.22Department of Gastroenterology, Erasmus Hospital, Free University of Brussels, Belgium.23INSERM;
Universite ´ Paris Diderot; Assistance Publique Ho ˆpitaux de Paris; Hopital Robert Debre ´, Paris, France.24Gastrointestinal Unit, Division of Medical Sciences, School of
Molecular and Clinical Medicine, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK.25Peninsula Medical School, Barrack Road, Exeter, EX2
5DW, UK.26Department of Medical and Molecular Genetics, King’s College London School of Medicine, 8th Floor Guy’s Tower, Guy’s Hospital, London, SE1 9RT, UK.
27Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.28The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus,
Hinxton, Cambridge CB10 1SA, UK.29IBD research group, Addenbrooke’s Hospital, University of Cambridge, Cambridge CB2 2QQ, UK.30Department of
Gastroenterology and Hepatology, University of Newcastle upon Tyne, Royal Victoria Infirmary, Newcastle upon Tyne NE1 4LP, UK.31Gastroenterology Unit, Radcliffe
Infirmary, University of Oxford, Oxford, OX2 6HE, UK.32Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge
Street, Boston, Massachusetts 02114, USA.33This study is a joint effort of the Wellcome Trust Case Control Consortium, the NIDDK IBD Genetics Consortium and the
French-Belgian IBD Consortium.34A full list of authors is provided in the Supplementary Note online. Correspondence should be addressed to
M.J.D. (mjdaly@chgr.mgh.harvard.edu).
962 VOLUME 40 [ NUMBER 8 [ AUGUST 2008 NATURE GENETICS
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© 2008 Nature Publishing Group http://www.nature.com/naturegenetics