Meta-Analysis Increases to 71 the Tally of Confirmed Crohn’s Disease Susceptibility Loci (original) (raw)

. Author manuscript; available in PMC: 2012 Mar 12.

Published in final edited form as: Nat Genet. 2010 Dec;42(12):1118–1125. doi: 10.1038/ng.717

We undertook a meta-analysis of six Crohn’s disease (CD) genome-wide association studies (GWAS) comprising 6,333 cases and 15,056 controls, and followed up the top association signals in 15,694 cases, 14,026 controls and 414 parent/offspring trios. Thirty new susceptibility loci meeting genome-wide significance (P-value <5×10−8) were identified. A series of in silico analyses highlighted particular genes within these loci and, together with manual curation, implicated functionally interesting candidate genes including SMAD3, ERAP2, IL10, IL2RA, TYK2, FUT2, DNMT3a, DENND1B, BACH2 and TAGAP. Combined with previously confirmed loci, the results described here identify a total of 71 distinct loci with genome-wide significant evidence for association with Crohn’s disease.

Crohn’s disease (OMIM #266600) results from the interaction of environmental factors, including the intestinal microbiota, with host immune mechanisms in genetically susceptible individuals. Along with ulcerative colitis (UC), it is one of the main subphenotypes of inflammatory bowel disease (IBD). GWAS have highlighted key CD pathogenic mechanisms, including autophagy and Th17 pathways. A meta-analysis of these early scans implicated 32 susceptibility loci, but only accounted for 20% of the genetic contribution to disease risk - suggesting that more loci await discovery1. Recognizing that an increased sample size would be required to detect these, we have expanded the International IBD Genetics Consortium (IIBDGC), approximately doubling the discovery panel size in comparison with the first meta-analysis.

The discovery panel for the current study comprised 6,333 CD subjects and 15,056 controls, all of European descent, with data derived from six index GWAS studies (for overview see Supplementary Table 1)2-6. Imputation using HapMap3 reference data allowed us to test for association at 953,241 autosomal SNPs. Our discovery panel had 80% power to detect variants conferring odds ratios ≥1.18 at the genome-wide significance level of P<5×10−8, assuming a minor allele frequency ≥20% in healthy controls. Under the same conditions, the sample size of our original meta-analysis had only 11% power1.

A quantile-quantile plot of the primary meta-statistic, using single-SNP Z-scores combined across all sample sets, showed a marked excess of significant associations (Supplementary Figure 1). A total of 2,024 SNPs within 107 distinct genomic loci, including all previously defined significant hits from our earlier meta-analysis, demonstrated association with P-values <10−5. A Manhattan plot is shown in Supplementary Figure 2. 51 of the regions, representing new loci associated at P<5×10−6, were followed up by genotyping the most significant SNPs in an independent panel of 15,694 CD cases, 14,026 controls and 414 parent/offspring trios (see Table 1 and Supplementary Table 2).

Table 1. Association results and in silico analyses for all 71 confirmed CD loci.

The upper tier lists new Crohn’s disease susceptibility loci (beyond the first international meta-analysis1) confirmed in the current study with a genome-wide significant P-value (P<5×10−8) in the combined analysis (discovery + replication sample.) and P<0.05 on replication. Results for replication are listed for all 39 loci which at the time of study design had not met P<5×10−8 plus at least nominal evidence of replication in an independent sample set. The 7 loci identified in subsequent studies are identified (see footnotes). The lower tier lists new data for SNPs/loci confirmed in the earlier meta-analysis1. Genomic positions were retrieved from NCBI’s dbSNP build v130. Linkage disequilibrium (LD) regions around focal SNPs were defined by extending the region to the left for 0.1 cM or until another SNP with P<10−5 was reached, in which case the process was repeated from this SNP. Right-hand boundaries were defined in the same way. We identified loci previously associated with other relevant traits by a manual literature search and using the NIH catalog of published Genome-wide association studies and the HuGe database (version 1.4) (accessed on May 28th 2010) 43,44.

UC – ulcerative colitis, AS – ankylosing spondylitis, Ps – psoriasis, PBC – primary biliary cirrhosis, T1D – type 1 diabetes, RA – rheumatoid arthritis, SLE – systemic lupus erythematosus, celiac – celiac disease, T2D – type 2 diabetes, MS – multiple sclerosis, Graves – Graves disease, AD – Alzheimer’s disease, MCV – mean corpuscular volume, ALL – acute lymphocytic leukemia, Lepr. – leprosy, SpA – spondyloarthritis, PD – Parkinson’s disease, CRC – colorectal cancer, CRP – C-reactive protein, TGs – triglycerides, PC – prostate cancer, HSV – human simplex virus, CAD – coronary artery disease, CLL – chronic lymphocytic leukemia, BD – bone density, B12 – serum vitamin B12 levels, HP – Helicobacter pylori, AA – alopecia areata, AITD – autoimmune thyroid disease, BC – breast cancer, BD – Behcet’s disease, GC – gastric cancer, Hep.C – hepatitis C susceptibility, SSc – systemic sclerosis, Myelo. – myeloproliferative disease, TB – tuberculosis, GvHD – Graft versus host disease, WBC – white blood cell count, HIES – hyper immunoglobulin E syndrome. Regional association plots for all 71 loci are shown in Supplementary Figure 4 and genotype data is shown in Supplementary Tables 3 and 4.

No. dbSNP ID Chr. Left - right (Mb) Risk allele - Allele frequency in control population P-value meta P-value repl. P comb. OR (95% CI); *Loci with evidence of > than 1 independent association Reported association Positional candidate genes of interest: some additionally highlighted byInline graphic 1kG cSNP(s) in LD; GRAIL (bold) ;eQTL (LOD score)
(a) New Loci meeting Genome–wide significance (P-value <5.0×10−8) in this study
1 rs2797685 1p36 7.66 - 7.89 A - 0.190 2.69×10−10 1.40×10−2 7.10×10−9 1.05 (1.01-1.10) Celiac VAMP3
2 rs3180018 1q22 153.24 - 154.39 A - 0.250 1.29×10−9 2.70×10−5 2.30×10−13 1.13 (1.06-1.19)* T2D, Asthma, PD SCAMP3, MUC1
3 rs1998598 1q31 195.58 - 196.21 G - 0.302 4.90×10−9 1.60×10−2 8.70×10−9 1.04 (1.00-1.09) Asthma DENND1B
4 rs3024505 1q32 204.87 - 205.10 T - 0.157 8.32×10−9 1.50×10−7 1.60×10−14 1.12 (1.07-1.17) T1D, UC, SLE, BD, Hep. C, IL10, IL19
5 rs13428812 2p23 25.30 - 25.46 G - 0.326 1.41×10−8 5.90×10−4 8.50×10−10 1.06 (1.03-1.10) DNMT3A
6 rs780093 2p23 27.24-27.71 T - 0.418 1.10×10−4 3.30×10−8 4.70×10−11 1.15 (1.10-1.21) CRP, Glucose, TGs Inline graphic GCKR
7 rs10495903 2p21 43.30 - 43.80 T - 0.129 7.70×10−8 2.90×10−8 1.60×10−14 1.14 (1.09-1.20)* T2D, PC Inline graphic THADA
8§ rs10181042 2p16 60.77 - 61.74 T - 0.420 6.61×10−9 N/A N/A 1.14 (1.09-1.19) RA, UC, Celiac C2orf74 (9.6), REL
9† rs2058660 2q12 102.17 - 102.67 G - 0.231 1.58×10−12 N/A N/A 1.19 (1.14-1.26) Celiac, Asthma, T1D, HSV IL18RAP, IL12RL2, IL18R1, IL1RL1
10 rs6738825 2q33 197.85 - 198.67 A - 0.473 1.82×10−7 1.60×10−3 3.50×10−9 1.06 (1.02-1.11) CAD Inline graphic PLCL1
11 rs7423615 2q37 230.76 - 230.94 T - 0.187 4.57×10−9 7.40×10−6 3.10×10−13 1.12 (1.07-1.18) CLL SP140 (8.8)
12 rs13073817 3p24 18.58 - 18.86 A - 0.322 8.20×10−7 1.00×10−3 6.70×10−9 1.08 (1.03-1.13)
13 rs7702331 5q13 72.49 - 72.62 A - 0.600 2.00×10−6 6.40×10−7 5.90×10−12 1.12 (1.07-1.17) TMEM174
14 rs2549794 5q15 96.11 - 96.45 C - 0.409 4.47×10−11 2.00×10−3 1.10×10−10 1.05 (1.02-1.09) AS, PD, T1D Inline graphic ERAP2, LRAP (47.2)
15 rs11167764 5q31 141.39 - 141.62 C - 0.796 1.10×10−9 4.20×10−3 2.00×10−9 1.06 (1.02-1.11) NDFIP1
16 rs359457 5q35 173.15 - 173.47 T - 0.571 5.25×10−8 3.30×10−6 2.50×10−12 1.08 (1.04-1.12) CPEB4 (6.1)
17 rs17309827 6p25 3.35 - 3.41 T - 0.639 6.16×10−7 3.10×10−4 6.70×10−9 1.10 (1.05-1.16) C6orf85
18 rs1847472 6q15 90.86 - 91.14 G - 0.658 3.63×10−6 1.40×10−4 5.10×10−9 1.07 (1.03-1.11) T1D, Celiac BACH2
19 rs212388 6q25 159.26 - 159.46 G - 0.393 1.41×10−7 2.40×10−5 2.30×10−11 1.10 (1.05-1.14) RA, Celiac, T1D↕ TAGAP
20 rs6651252 8q24 129.56 - 129.67 T - 0.865 2.29×10−6 2.40×10−13 3.90×10−18 1.23 (1.17-1.30)
21† rs4077515 9q34 138.27 - 138.54 T - 0.411 4.37×10−19 1.50×10−19 1.30×10−36 1.18 (1.13-1.22) UC, AS CARD9 (12.4), Inline graphic CARD9, Inline graphic SNAPC4
22 rs12722489 10p15 6.07 - 6.21 C - 0.852 8.51×10−6 5.20×10−5 2.90×10−9 1.11 (1.05-1.16) MS, T1D, Vitiligo, RA, AA, Asthma, AITD IL2RA
23 rs1819658 10q21 59.50 - 59.81 C - 0.774 1.41×10−7 1.10×10−10 9.10×10−17 1.19 (1.13-1.25) AD UBE2D1
24‡ rs1250550 10q22 80.67 - 80.77 G - 0.669 2.00×10−10 7.30×10−22 1.10×10−30 1.19 (1.15-1.23) Celiac, MS, Vitiligo, BC ZMIZ1
25 rs102275 11q12 61.28 - 61.44 C - 0.341 7.24×10−8 1.70×10−5 2.30×10−11 1.08 (1.04-1.12) CAD; Dyslipidemia FADS1 (5.0)
26 rs694739 11q13 63.58 - 64.05 A - 0.626 3.38×10−7 3.50×10−4 6.00×10−10 1.10 (1.05-1.16) AA PRDX5, ESRRA
27 rs2062305 13q14 41.72 - 42.00 G - 0.346 2.00×10−6 5.70×10−5 4.90×10−10 1.10 (1.05-1.15) BD, RA TNFSF11,TNFSF11 (5.9)
28 rs4902642 14q24 68.23 - 68.39 G - 0.584 2.00×10−7 4.50×10−5 1.60×10−10 1.07 (1.11-1.04)* Celiac, T1D ZFP36L1
29 rs8005161 14q35 87.28 - 87.71 T - 0.119 1.29×10−8 5.90×10−11 4.20×10−18 1.23 (1.16-1.31)* Inline graphic GALC, Inline graphic GPR65, GPR65
30 rs17293632 15q22 65.20 - 65.27 T - 0.233 1.41×10−13 2.00×10−8 2.70×10−19 1.12 (1.07-1.16) CAD, T2D SMAD3
31‡ rs151181 16p11 28.20 - 28.94 G - 0.386 1.10×10−10 1.20×10−3 1.50×10−11 1.07 (1.03-1.12) T1D, obesity, Asthma, CRC, SLE, RA Inline graphic APOB48R, Inline graphic IL27, Inline graphic SULT1A2, Inline graphic SULT1A1, Inline graphic SH2B1, EIF3C (11.3), IL27, LAT, CD19, NFATC2IP
32§ rs3091315 17q12 29.51 - 29.70 A - 0.723 1.70×10−13 N/A N/A 1.20 (1.14-1.26) HIV resistance CCL2, CCL7
33 rs12720356 19p13 10.26 - 10.50 G - 0.084 9.20×10−10 1.90×10−5 1.40×10−12 1.12 (1.06-1.19)* T1D, SLE, MS, HIES Inline graphic TYK2, TYK2, ICAM1, ICAM3
34‡ rs736289 19q13 38.42 - 38.47 T - 0.612 2.69×10−7 2.00×10−3 8.70×10−9 1.06 (1.02-1.11)
35‡ rs281379 19q13 53.78 - 53.97 A - 0.487 8.60×10−10 5.20×10−5 7.40×10−12 1.07 (1.04-1.11) B12, Norovirus, HP Inline graphic FUT2, Inline graphic RASIP1
36 rs4809330 20q13 61.65 - 61.95 G - 0.709 2.51×10−12 4.60×10−5 2.70×10−15 1.12 (1.06-1.18) Glioma Inline graphic RTEL1, TNFRSF6B, SLC2A4RG
37 rs181359 22q11 20.14 - 20.39 T - 0.203 6.31×10−13 2.30×10−6 4.80×10−16 1.10 (1.06-1.15) RA, Celiac, SLE, MCV Inline graphic YDJC
38‡ rs713875 22q12 28.23 - 29.00 C - 0.471 5.70×10−9 8.30×10−5 7.30×10−12 1.08 (1.04-1.13) T1D MTMR3
39 rs2413583 22q13 38.00 - 38.14 C - 0.830 1.70×10−10 9.50×10−18 1.10×10−26 1.23 (1.17-1.29) MAP3K7IP1
(b) Loci that met Genome–wide significance (P-value <5.0×10−8) in Barrett et al.
1 rs11209026 1p31 67.13 - 67.54 G - 0.932 1.00×10−64 N/A N/A 2.66 (2.36-3.00) UC, AS, Ps, PBC, GC, BD Inline graphic IL23R, IL23R
2 rs2476601 1p13 113.66 - 114.42 G - 0.907 4.47×10−9 N/A N/A 1.26 (1.17-1.37) T1D↕, RA, SLE, Ps, Vitiligo↕, AITD Inline graphic PTPN22, PTPN22
3 rs4656940 1q23 158.96 - 159.20 A - 0.801 6.17×10−7 N/A N/A 1.15 (1.09-1.21) SLE, RA CD244 (7.7), CD244, ITLN1
4 rs7517810 1q24 170.92 - 171.21 T - 0.246 1.51×10−15 N/A N/A 1.22 (1.16-1.28) Hep.C, SLE, SSc, T2D TNFSF18, TNFSF4, FASLG
5 rs7554511 1q32 199.11 - 199.32 C - 0.726 1.58×10−7 N/A N/A 1.14 (1.08-1.19) UC, celiac, MS Inline graphic C1orf106, KIF21B
6 rs3792109 2q37 233.81 - 234.23 A - 0.529 6.76×10−41 N/A N/A 1.34 (1.29-1.40) UC Inline graphic ATG16L1
7 rs3197999 3p21 48.16 - 51.73 A - 0.297 6.17×10−17 N/A N/A 1.22 (1.16-1.27) UC Inline graphic MST1, Inline graphic GPX1, Inline graphic BSN
8 rs11742570 5p13 39.88 - 41.00 C - 0.606 7.08×10−36 N/A N/A 1.33 (1.27-1.39) MS PTGER4
9 rs12521868 5q31 129.41 - 132.05 T - 0.422 1.41×10−20 N/A N/A 1.23 (1.18-1.28) Ps, Fibrinogen, Asthma,TB, UC Inline graphic SLC22A4, SLC22A5 (5.4),IRF1, CSF2, IL3
10 rs7714584 5q33 150.01 - 150.38 G - 0.088 7.76×10−19 N/A N/A 1.37 (1.28-1.47) TB IRGM
11 rs6556412 5q33 158.43 - 158.88 A - 0.332 5.37×10−14 N/A N/A 1.18 (1.13-1.24) Ps, SLE, Malaria, Asthma IL12B
12 rs6908425 6p22 20.60 - 21.25 C - 0.784 1.41×10−8 N/A N/A 1.17 (1.11-1.23) T2D, Ps, UC CDKAL1
13 rs1799964 6p21 31.49 - 32.98 C - 0.209 3.98×10−11 N/A N/A 1.19 (1.13-1.25) Multiple including UC Inline graphic MCCD1, Inline graphic LTA, HLA-DQA2, TNF, LST1, LTB, LTA, NCR3
14 rs6568421 6q21 106.50 - 106.67 G - 0.301 4.37×10−8 N/A N/A 1.13 (1.07-1.18)* SLE, RA PRDM1
15 rs415890 6q27 167.26 - 167.47 C - 0.522 2.51×10−12 N/A N/A 1.17 (1.12-1.22) RA, Graves CCR6
16 rs1456896 7p12 50.22 - 50.34 T - 0.69 1.20×10−8 N/A N/A 1.14 (1.09-1.20) AD, SLE, MCV, ALL IKZF1, ZPBP, FIGNL1
17 rs4871611 8q24 126.54 - 126.65 A - 0.609 1.51×10−12 N/A N/A 1.17 (1.12-1.23)
18 rs10758669 9p24 4.93 - 5.29 C - 0.349 1.00×10−13 N/A N/A 1.18 (1.13-1.23) UC, Myelo. JAK2
19 rs3810936 9q32 116.47 - 116.74 C - 0.682 1.00×10−15 N/A N/A 1.21 (1.15-1.27) UC, Lepr., SpA TNFSF15, TNFSF8
20 rs12242110 10p11 35.22 - 35.94 G - 0.315 1.10×10−09 N/A N/A 1.15 (1.10-1.20) UC CREM (6.4)
21 rs10761659 10q21 63.97 - 64.43 G - 0.538 4.37×10−22 N/A N/A 1.23 (1.18-1.29) BC ZNF365
22 rs4409764 10q24 101.26 - 101.33 T - 0.492 2.29×10−20 N/A N/A 1.22 (1.17-1.27) UC NKX2-3
23 rs7927997 11q13 75.70 - 76.04 T - 0.389 5.62×10−13 N/A N/A 1.17 (1.12-1.22) Atopy↕ C11orf30
24 rs11564258 12q12 38.42 - 39.31 A - 0.025 6.17×10−21 N/A N/A 1.74 (1.55-1.95) PD, Lepr. Inline graphic MUC19, LRRK2
25 rs3764147 13q14 43.13 - 43.54 G - 0.245 1.41×10−10 N/A N/A 1.17 (1.12-1.23) Lepr. Inline graphic C13orf31
26 rs2076756 16q12 49.02 - 49.41 G - 0.26 3.98×10−69 N/A N/A 1.53 (1.46-1.60) Lepr., Atopy, Blau, GvHD NOD2
27 rs2872507 17q21 34.62 - 35.51 A - 0.458 1.51×10−9 N/A N/A 1.14 (1.09-1.19) Asthma, UC, PBC, T1D, RA, WBC Inline graphic GSMDL, Inline graphic ZPBP2, ORMDL3 (20.3),IKZF3
28 rs11871801 17q21 37.57 - 38.25 A - 0.756 2.51×10−8 N/A N/A 1.15 (1.10-1.21) MS↕, obesity, HIES Inline graphic MLX, STAT3
29 rs1893217 18p11 12.73 - 12.92 G - 0.153 1.29×10−14 N/A N/A 1.25 (1.18-1.32) T1D↕, celiac PTPN2
30 rs740495 19p13 1.04 - 1.13 G - 0.247 8.13×10−12 N/A N/A 1.16 (1.10-1.21) GPX4, SBNO2
31 rs1736020 21q21 15.62 - 15.77 C - 0.579 9.33×10−12 N/A N/A 1.16 (1.11-1.21) UC
32 rs2838519 21q22 44.41 - 44.52 G - 0.391 2.09×10−14 N/A N/A 1.18 (1.13-1.23) Celiac, UC ICOSLG

Variants within 30 distinct new loci met a genome-wide significance threshold of P<5×10−8 for association with CD in the combined discovery plus replication panel, with at least nominal association in the replication panel (see Table 1). Two additional loci, encompassing the CARD9 and IL18RAP genes, had previously been reported as associated with CD in a candidate gene study7 and were here both replicated and confirmed at P<5×10−8. Five loci were identified at genome-wide significance in GWAS studies published subsequent to our replication experiment being designed. One, the FUT2 locus, was from a recent adult CD GWAS6. Four more (ZMIZ1, IL27 at 16p11, 19q13 and 22q12) were identified in a pediatric IBD population5, these replicating here in our current sample set. Two further loci had produced “suggestive” evidence of association with replication in our earlier study1. Here, these clearly exceeded the genome-wide significance threshold in the meta-analysis alone and, given the previous replication evidence, were not followed up further (see Table 1). Thus cumulatively, 39 additional loci can now be added to the 32 confirmed CD susceptibility loci identified at the time of the Barrett et al. study. We did not observe statistically significant heterogeneity of the odds ratios (Breslow Day test P-value <0.05 after Bonferroni correction; Supplementary Table 4) between the panels from our 15 different countries (Supplementary Tables 1 and 2) for any of the 71 loci. Nor was any evidence of interaction between the associated loci observed (Supplementary Figure 3).

Regional association plots of all 71 susceptibility loci including the underlying genes are shown in detail in Supplementary Figure 4, and complete genotype data including odds ratios and allele frequencies are shown in Supplementary Tables 3 and 4. Five loci had evidence for more than one independently associated variant (Table 1). While 6 of the 30 novel regions contain just a single gene, which is thereby strongly implicated in CD pathogenesis _(_e.g. SMAD3, NDFIP1 and BACH2), 22 include more than one gene within the associated interval (Table 1; two regions without any gene or gene prediction). We thus applied additional in silico analyses to refine the list of functional candidate genes further. These were:

  1. Interrogation of a publicly available expression quantitative trait loci (eQTL) database8. These analyses identified genes for which expression correlates with genotype at our most associated SNP (see Supplementary Results).
  2. Use of 1000 Genomes Project Pilot sequence data and HapMap3 to identify genes containing non-synonymous variants in strong LD (r2>0.5) with the focal SNP within each region (for details on coding SNP see Supplementary Table 5).
  3. Use of GRAIL9, to identify non-random and evidence-based connectivity between the genes in the 71 confirmed CD loci. Specifically, GRAIL evaluates each gene in a CD-associated locus for non-random correlation with genes in the other 70 loci via word-usage in PubMed abstracts related to the gene (see Figure 1).

Figure 1. Gene Relationships Across Implicated Loci (GRAIL) pathway analysis.

Figure 1

Links between genes at 23 of 71 Crohn’s disease associated loci which scored P<0.01 using GRAIL. Specifically, of the 71 CD-associated SNPs, 69 are in LD intervals containing or within 50 kb of at least one gene. In total, there are 355 genes implicated by proximity to these 69 SNPs. Each observed CD-association was scored with GRAIL, which takes all genes mapping within CD-associated intervals and evaluates for each whether it is non-randomly linked to the other genes, via word-usage in PubMed abstracts. 23 SNPs shown in the outer circle are P<0.01 hits - indicating that the regions which they tag contain genes which are more significantly linked to genes in the other 68 regions than expected by chance at that level. The lines between genes represent individually significant connections that contribute to the positive signal, with thickness of lines inversely proportional to the probability a literature-based connection would be seen by chance.

To accurately assess the statistical significance of this set of connections, we conducted simulations where we selected 1000 sets of 69 SNPs implicating in total 355 genes ±18 (5%) (selecting the SNPs randomly and using rejection sampling - only taking lists that implicated the same number of genes). Each of those 1000 sets were scored with GRAIL. The mean number of P<0.01 hits in a simulated list was 0.91 with a range in the 1000 sets from 0 to 11, suggesting that the likelihood of observing 23 hits with P<0.01 is far less than 0.1%.

Summary results of these analyses are shown in the rightmost column of Table 1. The highlighted genes are described briefly in Box 1, as are genes that constitute particularly noteworthy candidates from intervals containing one or few genes. While we believe that these evidence-based approaches are helpful in identifying likely functional candidates, in some instances the different techniques highlight different genes. This reflects uncertainty as to which is causal, and highlights the need for functional studies.

30 new signals were identified here beyond those described in the earlier meta-analysis1 and other subsequent publications. The new associations were driven primarily by increased power arising from the expanded sample size rather than improved imputation, as more than two-thirds of the novel loci identified here have good proxies (r2>0.8) on both earlier generation arrays (Illumina 300K and Affymetrix 500k Set). Extending this argument beyond the current analysis, it seems likely that many more loci of modest effect size still await discovery.

For many of the novel loci, associations have been reported previously in other complex diseases, comprising mostly chronic inflammatory disorders (Table 1). Such diseases can cluster both within families and individuals, reflecting shared genetic risk factors. For example, IBD and ankylosing spondylitis can co-segregate and both are associated with IL23R2,10 and TNFSF1511,12. The IL10 locus was previously associated with UC13 and was identified as a novel CD locus in the present study. Thus IL10 is a generic IBD locus, which is a functionally intuitive finding of potential therapeutic significance.

For loci previously associated with other inflammatory diseases the direction of effect in CD is usually the same, but in five cases the risk allele for one disease appears to be protective in another disease (see arrow symbol in “Reported association” column in Table 1). In most such instances, functional annotation suggests modulation of T cell and other immune pathways. Indeed, GRAIL highlights a number of such genes. These inverse associations may reflect overlap in the pathways by which the host regulates effector functions in defense and regulatory functions in self-tolerance. This is a delicate balance and, in the face of competing requirements, selection pressures may have conferred advantage for divergent alleles in a cell- and environmentally dependent manner.

The associated SNP rs281379 at 19q13, recently also identified by McGovern et al.6 is highly correlated (r2>0.80) with a common nonsense variant (rs601338 also known as G428A or W142X) at FUT2. This is classically referred to as the non-secretor variant, as individuals homozygous for this null-allele do not secrete blood group antigens at epithelial surfaces. Recently, non-secretors were identified as having near-complete protection from symptomatic GII.4 norovirus infection14 and the same null allele is identified here as a CD risk factor. This suggests one potential elusive link between infection and immune-mediated disease.

In contrast to the implication of coding variation in the FUT2 gene, our previous data demonstrated that most CD-associated SNPs were not in LD with coding polymorphisms1, suggesting that regulatory effects are likely to be a more common mechanism of disease susceptibility. Providing further direct evidence for this, a number of new eQTL effects were identified here (see Table 1 and Supplementary Results Section) – notably including CARD9 (LOD=12.4), ERAP2 (LOD=47.2) and TNFSF11 (RANKL) (LOD=5.9). The latter maps adjacent to but outside the associated recombination interval, suggesting another potential long-range cis-regulatory effect as previously described for PTGER4 in CD4. RANKL has pleiotropic immunological effects and also stimulates osteoclast activity. This finding may be relevant to the osteoporosis clinically associated with CD.

Given the importance of regulatory effects, it is intriguing that variants within the gene encoding a key mediator of epigenetic regulation, DNA methyltransferase 3a (DNMT3A), should be associated with CD. By inducing transcriptional silencing, DNMT3a is known to play an important role in immunoregulation. For example, it methylates IL-4 and IFN-γ promoters following T cell receptor stimulation, hence regulating T cell polarization15, and induces dynamic regulation of TNF-α transcription following lipopolysaccharide exposure in leukocytes16. Genetically determined alterations in DNMT3a activity could thus have far-reaching effects.

The 32 loci described up to 2008 explained approximately 20% of CD heritability. Adding the 39 loci described since increases the proportion of heritability explained to just 23.2%. This pattern of common alleles, explaining a logarithmically decreasing fraction of heritability (Figure 2), is consistent with a recent model of effect size distribution17, which predicted (based on the previous CD meta-analysis) that our current sample size would likely identify 48 new loci. Furthermore, it is likely that more high-frequency CD risk alleles of even smaller effect size remain unidentified: The same model predicts that 140 loci would be identified by a sample size of 50,000, but these would explain only a few more percent of CD heritability. It is clear, therefore, that larger GWAS alone will not explain all of the missing heritability in CD.

Figure 2. Cumulative fraction of genetic variance explained by 71 CD loci.

Figure 2

Cumulative fraction of genetic variance explained by the 71 CD loci reported here, ordered from largest to smallest individual contribution. Black points were identified pre-GWAS, green in first generation GWAS, blue in an earlier meta-analysis and cyan in this analysis. Inset shows a logarithmic fit to these data extrapolated to an extreme scenario where 20,000 independent common alleles are associated with disease. Even in this situation less than half of the genetic variance would be explained. This demonstrates that other types of effect (e.g. less common and rare alleles with higher penetrance) must also exist.

One key shortcoming of our current model of heritability explained by these loci is a direct consequence of the extent to which GWAS tag SNPs are often imperfect proxies for causal alleles, and thus substantially underestimate the true attributable risk. For example, the best tag SNP at the NOD2 locus in our meta-analysis appears to explain just 0.8% of genetic variance, whereas the three NOD2 coding mutations themselves account for 5%. If an analogous situation applies to even a small fraction of the other 70 CD susceptibility loci, the proportion of overall heritability explained will increase significantly. Indeed, one study of LD between tag SNPs and causal variants in the heritability of human height18 suggests that this effect might double the total fraction of heritability explained by GWAS SNPs. Coding variants identified here from the 1000 Genomes Project which are in strong LD with the focal SNPs in several of our regions (see Supplementary Table 4) thus now require direct assessment in order to explore this possibility.

Other factors will also account for the heritability gap, including uncertain epidemiological estimates of disease prevalence and total heritability, as well as our observation that several of the new regions contain more than one independent risk allele. The likelihood is that many more such effects will be identified. Indeed, detailed future analyses will play a key role in helping us to understand the absolute contribution of common causal alleles, as well as identifying less common variants and rare (even family-specific) mutations. By contrast, our lack of evidence for epistasis among the loci described here suggests that non-additive interactions among common risk alleles do not play an important role in the genetic architecture of CD.

The current study has approximately doubled the number of confirmed CD susceptibility loci. For many of these loci we have identified potentially causal genes, accepting that confirmation of their role must await detailed fine mapping, expression and functional studies. While the alleles detected only modestly affect disease risk, they continue to enhance our understanding of the genetic etiology of CD. Analysis for evidence of sub-phenotype associations represents an important future goal for the consortium. Thus, we are working towards sharing of detailed genotype and clinical data to allow this. In the meantime, extensive resequencing, together with large-scale fine mapping exercises using custom array-based technologies, are already underway and will further elucidate the pathogenic mechanisms of IBD.

Supplementary Material

Supplementary Text and Figures

Supplementary Table 3

Supplementary Table 4

Supplementary Table 6

Box 1.

Noteworthy genes within loci newly implicated in Crohn’s disease pathogenesis. N.B. Although we highlight these as interesting genes, we do not yet have data to confirm causality

Acknowledgements

We thank all subjects who contributed samples, and physicians and nursing staff who helped with recruitment globally. This study was supported by the German Ministry of Education and Research through the National Genome Research Network and infrastructure support through the DFG cluster of excellence “Inflammation at Interfaces”. Also the Italian Ministry for Health GR-2008-1144485, with case collections supported by the Italian Group for IBD and the Italian Society for Paediatric Gastroenterology, Hepatology and Nutrition. We acknowledge funding provided by Royal Brisbane and Women’s Hospital Foundation; University of Queensland (Ferguson Fellowship); National Health and Medical Research Council, Australia and by the European Community (5th PCRDT) and by the European Crohn’s and Colitis Organization. UK case collections were supported by the National Association for Colitis and Crohn’s disease, Wellcome Trust, Medical Research Council UK and Peninsular College of Medicine and Dentistry, Exeter. We also acknowledge the NIHR Biomedical Research Centre awards to Guy’s & St Thomas’ NHS Trust / King’s College London and to Addenbrooke’s Hospital / University of Cambridge School of Clinical Medicine. The NIDDK IBD Genetics Consortium is funded by the following grants: DK062431 (S.R.B.), DK062422 (J.H.C.), DK062420 (R.H.D.), DK062432 & DK064869 (J.D.R.), DK062423 (M.S.S.), DK062413 (D.P.B.M.), DK76984 (MD), and DK084554 (MD and DPBM), and DK062429 (J.H.C.). J.H.C. is also funded by the Crohn’s and Colitis Foundation of America; and SLG by DK069513 and Primary Children’s Medical Center Foundation. Cedars Sinai supported by NCRR grant M01-RR00425; NIH/NIDDK grant P01-DK046763; DK 063491; and Cedars-Sinai Medical Center Inflammatory Bowel Disease Research Funds. RW is supported by a clinical fellow grant (90700281) from the Netherlands Organization for Scientific Research; EL, DF and SV are senior clinical investigators for the Funds for Scientific Research (FWO/FNRS) Belgium. SB was supported by the “Deutsche Forschungsgemeinschaft” (DFG; BR 1912/5-1). JCB is supported by Wellcome Trust grant WT089120/Z/09/Z. Replication genotyping was supported by unrestricted grants from Abbott Laboratories Ltd and Giuliani SpA. We acknowledge the Wellcome Trust Case Control Consortium. We thank the 1958 British Birth Cohort and Banco Nacional de ADN, Salamanca, Spain who supplied control DNA samples. The CHS research reported in this article was supported by contract numbers N01-HC-85079 through N01-HC-85086, N01-HC-35129, N01 HC-15103, N01 HC-55222, N01-HC-75150, N01-HC-45133, grant numbers U01 HL080295 and R01 HL087652 from the National Heart, Lung, and Blood Institute, with additional contribution from the National Institute of Neurological Disorders and Stroke. A full list of principal CHS investigators and institutions can be found at http://www.chs-nhlbi.org/pi.htm. Other significant contributors: K. Hanigan, Z.-Z. Zhao, N. Huang, P. Webb, N. Hayward, A. Rutherford, R. Gwilliam, J. Ghori, D Strachan, W. McCardle, W. Ouwehand, M. Newsky, S. Ehlers, I. Pauselius, K. Holm, C. Sina, L. Baidoo, A. Andriulli and M.C. Renda.

Footnotes

Contribution of authors AF, DPBM, GRS, TA, JL, RR, JB, TH, AL, CGM, NP, JIR, PS, YS, LS, KDT, DW, CW, GKU, JDR, MD’A, RW, SV, RHD, JS, SS, VA, HH were involved in establishing DNA collections, and/or assembling phenotypic data; AF, DE, JCB, KW, TG, SR, CAA, LJ, MJD performed statistical analyses; DPBM, GRS, CWL, EMF, RNB, MB, TMB, SB, CB, AC, J-FC, MC, SC, TD, MdV, RD’I, MD, CE, TF, DF, RG, JG, AVG, SLG, JH, DH, J-PH, DL, IL, ML, AL, CL, EL, CM, WN, JP, AP, DDP, MR, PR, JS, MS, FS, AHS, PCFS, SRT, LT, TW, SRB, RW, SK, AMG, JCM, SV, RHD, MSS, JS, SS, JHC, VA recruited patients; AF, DPBM, TB, SB, KT, MG, GM supervised laboratory work; AF, DPBM, JCB, KW, SB, RHD, JS, SS, JHC, MJD, MP contributed to writing the manuscript. All authors read and approved the final manuscript before submission.

All authors declare no financial interest.

References

Associated Data

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Supplementary Materials

Supplementary Text and Figures

Supplementary Table 3

Supplementary Table 4

Supplementary Table 6