University of Groningen
Genetic susceptibility for inflammatory bowel disease across ethnicities and diseases
van Sommeren, Suzanne
DOI:
10.33612/diss.100597247
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van Sommeren, S. (2019). Genetic susceptibility for inflammatory bowel disease across ethnicities and
diseases. Rijksuniversiteit Groningen. https://doi.org/10.33612/diss.100597247
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CHAPTER
6
Extra-intestinal
manifestations and complications
in inflammatory bowel disease –
From shared genetics to
shared biological pathways.
Suzanne van Sommeren*, Marcel Janse*, Juha Karjalainen*, Rudolf Fehrmann, Lude Franke*, Jingyuan Fu* and Rinse K. Weersma* Inflammatory Bowel Diseases 2014;20(6):987–94
ABSTRACT
Background: The clinical presentation of the inflammatory bowel diseases (IBD) is extremely heterogeneous and is characterized by various extra-intestinal manifestations and complications (EIM). Increasing genetic insight for IBD and EIM shows multiple shared susceptibility loci. We hypothesize that, next to these overlapping genetic risk loci, distinct disease pathways are shared between IBD and EIM.
Methods: The overlapping genetic risk loci for IBD and its EIM were searched for in literature. We assessed shared disease pathways by performing an extensive pathway analysis by protein- protein interaction (PPI) and co-transcriptional analysis, using both publically available and newly developed databases. Results: Reliable genetic data was available for primary sclerosing cholangitis (PSC), ankylosing spondylitis (AS), decreased bone mineral density (BMD), colorectal carcinoma (CRC), gallstones, kidney stones and deep venous thrombosis (DVT). We found an extensive overlap in genetic risk loci, especially for IBD and PSC and AS. We identified 370 PPIs, of which 108 are statistically spe-cific. We identified 446 statistically specific co-transcribed gene pairs. The interactions are shown to cluster in specific biological pathways.
Conclusion: We show that the pathogenetic overlap between IBD and its EIM extends beyond shared risk genes to distinctive shared biological pathways. We define genetic background as a risk fac-tor for IBD-EIM alongside known mechanisms as malabsorption and medication. Clustering patients based on distinctive pathways may enable stratification of patients to predict development of EIM.
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INTRODUCTION
The inflammatory bowel diseases (IBD) are complex diseases encompassing multiple spe-cific sub-phenotypes. Included in IBD’s disease spectrum are various extra-intestinal mani-festations and complications (together named EIM). In addition to having a high incidence in IBD patients, the EIM can actually cause more morbidity than IBD itself. Frequently occurring EIM are immune mediated and involve multiple organ systems like the skin (erythema nodosum, pyoderma gangrenosum), the eyes (uveïtis, epis-cleritis), the liver (primary sclerosing cholangi-tis, PSC) or spine (ankylosing spondylicholangi-tis, AS).1
The skin and eye diseases occur during active in-testinal disease, while PSC and AS can also occur while IBD itself is in a quiescent state. Decreased bone mineral density (BMD), gallstones and kid-neystones are considered to be mainly driven by the (metabolic) consequences of malabsorp-tion and chronic use of steroids. Longstanding active disease predisposes to the development of colorectal carcinoma (CRC), necessitating frequent surveillance colonoscopies.2 Despite
this knowledge, it remains unclear why the IBD phenotype includes so many EIM, in what way disease pathogenesis is overlapping between IBD and EIM and why certain patients develop a particular EIM and others do not. One possible answer to these questions is an overlap in ge-netic architecture; this would explain why these EIM co-occur so often with IBD and moreover variation in the genetic make-up in individuals could partly explain the variability in disease phenotype. It has been demonstrated before that many immune mediated diseases share a genetic background, and because IBD and its EIM co-occur so frequently we hypothesize that they have a common genetic background.3
It has become clear that IBD is not caused by an abnormality in a single gene leading to a single uniform disease, but is a consequence of the perturbations of complex pathways leading to multiple specific sub-phenotypes.1 Recently
there has been tremendous progress in unravel-ling the genetic background of ulcerative colitis and Crohn’s disease, to date 163 independent
genetic susceptibility loci have been identified. The identified single genetic risk factors have been shown to cooperate in disease relevant pathways.4 Similarly, genetic variants
predis-posing to many EIM have been established, of which some are shared with IBD. We hypothe-size that, next to these overlapping genetic risk loci, disease pathways are shared between IBD and EIM.
To understand how these genetic variants pre-dispose to IBD-EIM, we performed a survey of overlapping genetic loci. For this we included the following EIM or complications of IBD: an-kylosing spondylitis, primary sclerosing cholan-gitis, decreased bone mineral density, colorectal carcinoma, erythema nodosum, pyoderma gan-grenosum, uveïtis, episcleritis, kidney stones, gall stones and venous thrombosis. Next we an-alyzed co-regulation between all pairs of genes within IBD loci and EIM loci (with available and reliable genetic data), for protein-protein inter-actions (PPI) and co-transcription using large databases (including > 80,000 human Affyme-trix mRNA expression datasets).
We show that the pathogenetic overlap be-tween IBD and its EIM is partly driven by a shared genetic predisposition and extends be-yond purely shared risk genes to distinct shared biological pathways.
MATERIAL AND METHODS
Literature search and selection of associated loci
Genetic association data for Inflammatory Bowel Disease was extracted from the most re-cent analyses.4 An extensive literature search
was performed in Pubmed and the GWAS cat-alogue (www.genome.gov/gwastudies) till Oc-tober 2011 to assess available data on genetic susceptibility of the EIM. For EIM all performed GWAS and GWAS meta-analyses were included. We also included large candidate studies if lim-ited GWAS data was available. From GWAS we included SNPs with reported genome wide sig-nificance (in most studies defined as p < 5 × 10–8).
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Chapter 6
p < 1 × 10–4. For PSC, we also included the
GP-BAR1 locus because this locus showed func-tional evidence.5 When there were only small
candidate gene studies available or with con-flicting results we did not include the identified susceptibility loci for the pathway analyses. All well- established genetic loci in IBD and the EIM were included and genetic overlap was assessed as presented in Supplementary table 1.
Mapping SNPs to Genes
For the co-transcriptional and protein-protein interaction analyses all candidate genes in the loci were included. Therefore, we assessed which genes are located at associated loci in IBD and EIM. The disease-associated SNPs were linked to proximate candidate genes in linkage disequilibrium (LD) with them, using a previously described approach.6 We
down-loaded the recombination hotspot and LD information from www.hapmap.org for CEU population (release 28) and genome build hg18. The information of gene positions was based on genome build hg18 and was downloaded from UCSC Genome Browser. For each of the associated SNPs, we first defined the disease locus as the region containing the SNPs with LD r2 > 0.5 to the associated SNP and then
ex-tended it to the nearest recombination hotspot. This region was further extended 100 kb on each side to include the potential regulatory regions for genes. If any transcript isoform of a gene overlaps with the defined disease locus, this gene was included as a candidate gene. Thus a ith IBD locus with n number of candidate genes
was defined as IBDi {g1,g2,…gn}; and a jth EIM
locus with z number of candidate genes was defined as EIMj{ga, gb, …, gz}.
PPI between IBD and EIM loci
We used the PPI database that was described by Rossin et al.6 and Lage et al.7 and extracted all
the direct interactions between the candidate genes in IBD loci and the candidate genes in EIM loci, for example between the given ith IBD
loci IBDi {g1,g2,…gn} and the given jth EIM loci
EIMj {ga, gb, …, gz}. To assess the specificity of the
interactions between loci, we further calculated
p-values empirically by testing interactions be-tween the EIMj loci and the mimicked random IBDi locus with similar number of genes: if
n ≤ 10, the mimicked locus must contain the same n number of genes; if n > 10, the mim-icked random locus could contain the number of genes within 10% variation (i.e., gene numbers within 0.9 × n and 1.1 × n). The random IBDi lo-cus was mimicked 1,000 times. We then scored, out of 1,000 random loci, how many times we could observe at least one interaction between the EIMj locus and the random IBDi locus and calculated the empirical p-value. The signifi-cance threshold was controlled at 0.05.
Co-transcriptional interactions between IBD and EIM loci
In order to assess the similarities in transcription between genes in IBD loci and EIM loci, we utilized a new gene co-transcriptional network based on gene expression (www.genenetwork.nl). First, to derive a regulatory model of the hu-man transcriptome, we performed principal component analysis (PCA) on microarray ex-pression data on three species (Homo sapiens, Mus musculus and Rattus norvegicus) pub-licly available in Gene Expression Omnibus (GEO). We used data for samples hybridized to HG-U133 Plus 2.0, HG-U133A, MG-430 2.0 and RG-230 2.0 Affymetrix Genechip platforms and performed PCA on the probe correlation matrix of each of the four platforms. We ended up with 777, 377, 677 and 375 robust princi-pal components and eigenvectors respectively, each component describing an underlying factor that regulates expression. We treated each of these components as being equally interesting: While the first components from each of the four platforms explain most variation in expression, they overshadow other (subtle) relationships that also exist within this data, which have been captured by other components. Conventional co-expression analysis, which uses e.g. Pearson correlations between the expression levels of pairs of genes and does not use PCA, is likely to miss such subtle relationships. Here we use a correlation measure that uses the 2,200 com-ponents between pairs of genes, and is able to
85
identify such relationships. First, we mappedeach probeset on each of the four platforms to a gene and averaged eigenvector coefficients over probesets mapping to the same gene. Probes with no or unambiguous mapping (due to e.g. cross-hybridization) were excluded from fur-ther analysis. Second, we calculated for each pair of human genes a Pearson product-moment correlation coefficient over the 2,200 eigenvec-tor coefficients of each platform that contained probe sets for the genes or, for the mouse and rat platforms, their most similar orthologs. Both probeset and gene ortholog mapping information were downloaded from Ensembl Biomart (Ensembl release 65). The correlation coefficients were further converted to Z-scores to account for different numbers of available eigenvectors for pairs of genes due to missing orthologs. The Z-score for a pair of genes can be positive or negative and describes the similarity of their regulation. The genes (19,997 in total) form the nodes and the correlation coefficients the edges of the network.
We used this co-transcriptional network to find significantly co-transcribed genomic re-gions implicated by SNPs associated with IBD or EIM. First, we determined regions of interest for each phenotype. For each SNP associated with a phenotype, we defined implicated genes by identifying furthest SNPs in both the 3’ and 5’ directions in LD with it (r2 > 0.5 based on CEU
HapMap 2), and extending the region first to the nearest recombination hotspot and then an additional 100 kb in both directions. Each gene in the co-regulation network overlapping with this region was considered implicated by the SNP and part of the locus. Overlapping loci were merged together.
We then examined the potential co-transcrip-tion between the regions associated with IBD and those associated with each of the EIM by finding pairs of genes that show the strongest co-transcription in either direction in each pair of loci between the phenotypes. To ascertain whether genes in a locus were often co-tran-scribed with genes in other regions, we ran a permutation test with 1,000 permutations randomly picking a region from the genome
with a similar centimorgan range and a similar number of genes as the IBD locus and repeating the procedure with the random locus replacing the IBD locus. We then applied a 5% false dis-covery rate to eliminate potential false positive gene pairs.
Biological interpretation of interactions
To identify pathways in which the interacting genes of IBD and the different EIM play roles, we used DAVID Bioinformatics database.8 DAVID
uses several resources like GO-terms, KEGG – the Kyoto encyclopedia of genes and genomes9
and BioCarta (www.biocarta.com) to cluster genes together in pathways.
RESULTS
Genetic overlap
We first assessed genetic overlap between IBD and all EIM: AS, PSC, BMD, CRC, erythema nodosum, pyoderma gangrenosum, uveïtis, episcleritis, kidney stones, gall stones and ve-nous thrombosis. Most robust genetic data was available for AS, CRC, BMD and PSC. For gall-stones, kidney stones and deep venous throm-bosis limited genetic data was available, only candidate gene studies were available, while for the skin EIM (erythema nodosum, pyoderma gangrenosum) and eye EIM (uveïtis, episcleritis) no genetic data was available (table 1). Supple-mentary table 1 shows all associated loci per EIM. The classical immune-driven diseases AS and PSC have the largest number of risk loci that are shared with IBD: In AS 13 out of 18 known risk loci are shared with IBD, for PSC this is 10 out of 14. In CRC 3 out of 15 risk loci are shared with IBD and for BMD this is 1/23. For deep venous thrombosis two loci are identified, the traits gallstones and kidney stones both have one identified genetic risk locus. None of these loci are also associated with IBD. Figure 1 shows all the shared loci between any EIM and IBD per chromosome. Because the HLA and CARD9 loci are shared between IBD, AS and PSC there are 25 unique shared loci across all EIM. Supple-mentary table 1 lists all established EIM loci.
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Chapter 6
PPI and co-transcriptional regulation
We then searched for statistical significant PPIs and co-transcriptional interactions between IBD genes and EIM genes. From the PPI database we identified 370 PPIs between IBD and EIM loci, excluding PPIs between shared IBD-EIM loci. The (extended) HLA locus is involved in 57 out of 370 interacting loci. After 1,000 permutations of testing PPIs between the tested EIM locus and a mimicked IBD locus 86/370 PPIs turned out to be significantly specific (figure 2). Interacting IBD-EIM loci can hold multiple PPIs between different genes in the loci. These 370 interact-ing loci hold a total of 915 PPIs between IBD and EIM genes (441 of these PPIs involve a gene originating in the HLA locus).
From the co-transcriptional network we created we identified 10,890 significant co- transcribed genes between IBD and its EIM. After 1,000 permutations 446 turned out to be significantly specific (Supplementary figure 1).
Pathway annotation
We then annotated the biological pathways in which the interacting genes of IBD and the dif-ferent EIM play roles, by using the DAVID Bio-informatics database. To ensure the reliability of the observed pathways, an extensive liter-ature search was performed. Due to the small amount of associated loci in gallstones, venous thrombosis and kidney stones, few interactions
were found and no defined pathways were es-tablished. For AS, PSC, CRC and BMD we find several interesting pathways where genes with interactions via PPI or co-transcription play roles. These are highlighted in figure 3 and we discuss them in detail further on.
DISCUSSION
We aimed to give an overview of the current genetic data on IBD and its EIM and provide additional downstream pathway analyses us-ing publicly available and new locally developed databases. The data as shown here are first of all important to understand the pathogenetic mechanisms that lead to the co-occurrence of immune mediated extra-intestinal manifes-tations or the development of long-term com-plications. Second, in the future clustering pa-tients based on distinctive involved pathways may enable stratification of patients to predict development of EIM and investigate specific screening protocols.
Colorectal Carcinoma
The most prominent shared pathway between IBD and CRC is regulation of the intrinsic epi-thelial barrier integrity. Central in this pathway is CDH1; encoding E-cadherin, which anchors cells together in adherens junctions and is
Table 1. Available genetic data for EIM and complications of IBD.
EIM/complications No. of genetic studies
Colorectal carcinoma 910–18
Decreased bone mineral density 919–27
Primary sclerosing cholangitis 428–31
Ankylosing spondylitis 432–35 Gall stones 136 Kidney stones 137 Venous thrombosis 138 Erythema nodosum 0 Pyoderma gangrenosum 0 Uveitis 0 Episcleritis 0
87
102
Figure 1
. Chromosomes with risk loci for IBD
an
d its EIM.
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at
io
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und
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y
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e
1.
PPI and co
-tran
scription
al regulation
We
th
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ed
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r s
tati
sti
cal s
igni
fic
ant P
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and
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-tr
ans
cr
ipti
onal inter
actions
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ab
ase w
e ident
ified 370 PPIs
be
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en IBD
and
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ci
, e
xc
lud
ing P
PIs
be
twe
en s
har
ed
IBD
-EIM loci. The (extend
ed
)
HLA
locus is involved in 57 out of 370 interacting loci. After 1000
permutat
ions of
testing PPIs between the tested EIM locus and a mimicked IBD lo
cus 86/370 PPIs turned
out
to b
e significa
nt
ly specific (Figure 2). Int
era
ct
ing IB
D-EIM loci can hold multiple PPIs
be
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ne
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e lo
ci
. T
he
se
3
70
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h
old
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tal o
f 9
15
PPIs
between IBD and EIM genes (441 of these PPIs involve a gene ori
ginating in the H
LA
lo
cus
).
1 ● ● ● ● ● ●● ●● ●●● ● 2 ● ● ● ●● ● ●● 3 ● ● 4 ● ●●●● 5 ● ● ● ● ●●●●●● ● ● 6 ● ● ● ● ● ●●● 7 8 9 ● ● ● ● ● 10 ● ● 11 12 ● ● 13 14 15 16 ● ● ● ● 17 ● ●● 18 ● ●● ● ●● ●●● 21 ● ●● 22 23 24 19 KIF21B IL23R TNFRSF14 GPBAR1 MST1 IL2/IL21 PT GER4 ERAP IL12B MHC CDK AL1 CARD9 JAK2 IL2RA MUC19 ZB TB40 REL CDH1 FUT2 RHPN2 PSMG1 ST AT3 SM AD7 SOCS1 B3GNT 20 ● ● ● ●
Colorectal carcinoma Ankylosing spondylitis Bone mineral density Primary sclerosing cholangitis
In
flammatory bowel disease
● Figur e 1. Chr omosomes
with risk loci for IBD and its EIM. Eac
h d ot r epr esen ts the posi -tion of an associa te d loc us on th e c hr omos om e. F or sha re d
risk loci the c
andida te g ene is dep ic te d. C omp le te lo ci in fo r-ma tion c an be f ound in s upple -men tar y t able 1.
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Chapter 6
associated to both IBD and CRC.39 Other
inter-acting genes influence epithelial barrier func-tion by regulating the cellular actin cytoskeleton (RHPN2, FMN1, RHOA, ARPC2, CAPN10) or es-tablishing attachment of the cytoskeleton to the basement membrane (DAG1) or in the basement membrane (LAMB1, LAMA5).40 The TGF-β
sig-naling pathway harbors many genes associated
to CRC or IBD, illustrating the crucial regulatory role TGF-β plays in different relevant pathways including epithelial barrier function and the immune system.41 The regulatory functions of
TGF-β are shared with Wnt signaling, which is an established pathway in CRC and has recently been associated with UC-related CRC.42
Figure 2: PPI between IBD loci and EIM loci.
Each dot represents the position of a locus associated to IBD or one of the EIM on the chromosomes. Names of the can-didate genes in the associated loci are presented on the outer side of the circle. The colored lines represent the PPIs that are significant after permutations between IBD genes and EIM genes, different colors are used for the different EIM. The grey lines represent protein-protein interactions that are not significant after permutations.
89
Bone Mineral Density
Wnt signaling genes are abundantly present in BMD associated loci (WNT16, CTNNB1, GPR177, LRP5) and have interactions with IBD genes like CDH1 and SMAD3. Wnt signaling has been shown to influence the RANK/RANKL/ OPG (TNFSF11, TNFRSF11A, TNFRSF11B) pathway.43 TNFSF11 is associated with BMD
and was previously identified as a CD locus,44
however, in the most recent IBD analyses4 this
locus could not be replicated. Therefore, the association of this locus with IBD remains ques-tionable. TNFSF11 has interactions with its re-ceptor TNFRSF11A and the inhibiting decoy receptor TNFRSF11B, which are both associated to BMD. This pathway is involved in both bone
Figure 3. Summary of shared biological pathways in IBD and its EIM.
Each square represents an associated gene. An IBD gene with multiple interactions across the EIM is depicted multiple times. A blue line represents the most significant co-transcriptional interaction between two genes. PPIs significant after permutations are represented by black lines, non-significant PPIs by grey lines. Pathways are encircled, sub-pathways within a bigger pathway are depicted in gray circles.
NKD1 CCR6 IL7R MAP3K7IP1 TNFSF11 BCL2L11 FMN1 LIMA1 UBASH3A RHPN2 LAMA5 BACH2 PTPN2 IL2 IL2RA ICOSLG CIITA IL15RA HLA −DRB1 LAT PRDM1 EVI1 IFNG SMAD7 AP1M2 CDH1 CBLL1 ASH1L GNA12 MUC1 ZMIZ1 EXOC3 RUNX3 LTBR NPEP PS TAPBPL ERAP1 TNFRSF1A JAK2 IL23R IL10 IL7R IFNG IL12RB2 BMP4 GREM1 SMAD3 POU2AF1 CD19 SLAMF7 PTPN22 ARPC2 LAMB1 IRF8 FOXO1 CAPN10 RHOA DAG1 IL10 IFNG IL21 LTB IRF1 IFNG IRF5 DAP INPP5D GPR65 TNF IRF8 WLS JAG1 CTNNB1 SFRP4 SOCS1 WNT16 RHOA NKD1 STAT3 TUBG1 VEGFA SMAD3 JAK2 CDH1 HNF4A TYK2 IL12B IL26 IL4R IL21R JAK2 IL2 TYK2 ITGB7 FCGR2B PFDN5 PTPN22 BAT3 CCL2 TNFRSF11A TNFRSF11B CCR6 CCL13 IL2RA TNFSF11 T cell apoptosis Basal membrane Cytoskeleton regulation Adherens and tight junctions Cytoskeleton-adherens junction connection JAK-STAT signaling Adaptive immune system Adaptive immune system Wnt signaling TGF-beta signaling Apoptosis Wnt signaling RANK-RANKL-OPG pathway MHC-I antigen binding T cell signaling In ammatory bowel disease Primary sclerosing
cholangitis Bone mineral density
Ankylosing spondylitis Colorectal
carcinoma
In ammatory bowel disease Primary sclerosing cholangitis Shared in ammatory bowel disease and primary sclerosing cholangitis
Bone mineral density Shared in ammatory bowel disease and bone mineral density Ankylosing spondylitis
Shared in ammatory bowel disease and ankylosing spondylitis Colorectal carcinoma Shared in ammatory bowel disease and colorectal carcinoma
Intrinsic epithelial barrier function
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Chapter 6
homeostasis as regulating T cell – dendritic cell communications, dendritic cell survival and lymph node organogenesis, crucial in IBD pathogenesis.45 We see many co-regulation
between genes in this pathway and genes that are involved in the adaptive immune system (e.g. PTPN22, CCR6). These findings highlight possibilities for e.g. drug repositioning for Denosumab (Amgen/GlaxoSmithKline) which targets TNFSF11 and is a marketed drug for the treatment of postmenopausal women at high risk of fracture with osteoporosis.46 The gene
ITGB7, encoding integrin beta-7, which asso-ciates with alpha-4 to form integrin alpha-4/ beta-7, resides in a BMD associated locus and interacts with the IBD gene CDH1. Natalizumab (Biogen), registered for use in Crohn’s disease, blocks homing of lymphocytes to vascular en-dothelial cells of the gastro-intestinal tract via MADCAM1 on the endothelium and integrin alpha-4/beta-7 on lymphocytes.47 These
find-ings highlight the importance of investigating pathways associated with the broader IBD-EIM phenotype.
Primary Sclerosing Cholangitis and Ankylosing Spondylitis
Most striking in the analysis for PSC and AS is the large number of overlapping loci. Even if we focus on PSC and AS genes that are not shared with IBD we find many interactions in pathways that are shared with IBD. PSC genes and their connected IBD genes play several roles in T cell signaling, like T cell apoptosis (UBASH3A, BCL2L11, FOXO1 and IRF8) and the JAK-STAT signaling pathway (SOCS1, JAK2, STAT3 and TYK2).48,49 For AS the largest shared pathway
is the T cell apoptosis pathway, with many inter-actions with IBD genes. The AS associated genes TAPBPL and NPEPPS function in the same pro-cess as the AS-IBD shared gene ERAP1, namely the process of protein antigen binding to the MHC-I molecules which is likely to be crucial to both AS and IBD pathogenesis.50
Given the fast moving field of IBD genetics we do not provide a definitive analysis. Very recently the number of shared genes between IBD and other diseases has increased with the
publication of several papers using a customized GWAS chip (Immunochip, Illumina, San Diego, CA) focussed on immune mediated diseases.4,51
At the time of the current analyses these data were not available but the number of shared pathways is expected to increase substantially. Large-scale analyses of completely phenotyped IBD cohorts (including reliable data on EIM) are necessary to further investigate whether pa-tients with specifics EIM are enriched for risk genes or risk pathways as presented here.
In conclusion, we show that the pathoge-netic overlap between IBD and its EIM or com-plications extends substantially beyond purely shared risk genes to shared biological pathways. We identified numerous statistically significant interactions clustering in several distinct bio-logical pathways between IBD and the different EIMs. Hereby we further highlight the genetic background as risk factor for IBD-EIM next to known mechanisms as malabsorption, chronic inflammation or medication.
SUPPLEMENTARY DATA
Supplementary data are available online: Supplementary table 1 http://links.lww.com/ IBD/A456
Supplementary figure 1 http://links.lww.com/ IBD/A457
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