Through a genome-wide scale comparison between meta-analysis of GWAS and immunochip datasets in CD and leprosy in Asian populations, we identified 9 common susceptibility loci to CD and leprosy.
Previous studies examining well established European IBD susceptibility loci in leprosy dataset of Chinese population identified 6 common susceptibility loci only.1Previoulsy reported 6 common loci were overlapped with the 9 common loci identified in this study, and their directions of effects were consistent with current study. Despite its modest sample size, our study identified 3 more common loci to CD and leprosy (including the IL23R, ZNF365, NOD2 loci), probably due to the discoveries made in similar ethnic populations. Although our study represents
the largest samples of CD or leprosy in East Asians, due to the differences in SNP arrays used, especially East Asian CD datasets were generated using Immunochip v1.0, the number of common SNPs between CD and leprosy were limited. Identification of common susceptibility loci between CD and leprosy can be accelerated by using high-density SNP array with uniform genome coverage.
Our study has indicated that 7 of 9 common susceptibility loci for CD and leprosy have the opposite genetic effect. Only the two susceptibility loci (LACC1, RIPK2loci) showed consistent associations between CD and leprosy, in keeping with published data.5 LACC1determined the mitochondrial and nicotinamide adenine dinucleotide phosphate-oxidase-dependent production of reactive oxygen species, bactericidal activity and inflammasome activation in macrophages.19The loss-of-function for LACC1p.Ile254Val variant (rs3764147) decreased the function of microbial recognition and responses mediated by host pattern recognition receptors which could result in dysfunction of cytokines and bacterial clearance.28It is intriguing to see that the LACC1rs2121033 (high LD with rs3764147 p.Ile254Val; r2 = 0.94) confers protection against Behçet’s disease, opposite of CD and leprosy.29 This is the first report in East Asians that CD is associated with NOD2 and RIPK2. The NOD2-RIPK2 pathway has been implicated in the recognition of pathogens. Of note is that the microbial sensor NOD2showed the opposite genetic effects between CD and leprosy, while RIPK2showed consistent effects between CD and leprosy.
The eQTL analyses for the CD and leprosy risk variants from the 2 loci with concordant effects showed consistent expression pattern of RP11-37B2.1at 8q21.3 in skin and LACC1and CCDC122 at 13q14.1 in lymphocytes and nerve tissue. In the 7 common susceptibility loci with opposite effects in CD and leprosy, the CD and leprosy lead risk variants showed opposite effects on expression of a few candidate genes among multiple genes in each locus. Due to the limitation in SNP coverage, we’re not able to fine map the candidate loci and integrate fine mapping with eQTL data.
Of 6 susceptibility loci involved in the most significant pathway (cytokine and cytokine receptor) in CD, 4 loci (IL23R, IL18RAP, IL12B, TNFSF15loci) were common susceptibility loci to CD and leprosy. In pathway analysis of leprosy, only 2 common susceptibility loci (RIPK2, NOD2 loci) to CD and leprosy were involved in the most significant pathway (JNK
26
and p38 MAPK were significantly increased in immune cells within the inflamed intestinal lamina propria of CD patients than healthy controls.30 The pathway analysis suggested that common susceptibility loci to CD and leprosy might be playing important roles in causation of immunodeficiency as well as autoimmunity.
As leprosy has two distinct clinical manifestations, designated as tuberculoid and lepromatous, it would be interesting to examine which type shares common genetic susceptibility loci with CD, or specific subtype of CD. This could give information on proportion of CD cases with mycobacterial cause. Due to our modes sample size and lack of detailed clinical information of leprosy, we were not able to perform such analysis.
Web Resources
The URLs for data presented herein are as follows:
The 1000 Genome Project, http://www.1000genomes.org/
GCTA cnsgenomics.com/software/gcta/
IIBDGC, www.ibdgentics.org
Genotype-Tissue Expression (GTEx) project, http://www.gtexportal.org/home
28 References
1. Zhang F-R, Huang W, Chen S-M, Sun L-D, Liu H, Li Y, et al. Genomewide Association Study of Leprosy. New England Journal of Medicine. 2009;361: 2609–2618.
doi:10.1056/NEJMoa0903753
2. Zhang F-R, Liu H, Chen S-M, Low H-Q, Sun L-D, Yong Cui, et al. Identification of Two New Loci at IL23R and RAB32 That Influence Susceptibility to Leprosy. Nature Genetics.
2011;43: 1247–51. doi:10.1038/ng.973.
3. Liu H, Irwanto A, Hongqing Tian, Fu X, Yu Y, Yu G, et al. Identification of IL18RAP/IL18R1 and IL12B as Leprosy Risk Genes Demonstrates Shared Pathogenesis between Inflammation and Infectious Diseases. The American Journal of Human Genetics. 2012;91: 935–
41. doi:10.1016/j.ajhg.2012.09.010.
4. Liu H, Fangfang Bao, Irwanto A, Fu X, Nan Lu, Yu G, et al. An Association Study of TOLL and CARD with Leprosy Susceptibility in Chinese Population. Human Molecular Genetics.
2013;22:4430–37. doi:10.1093/hmg/ddt286.
5. Liu H, Irwanto A, Fu X, Yu G, Yu Y, Sun Y, et al. Discovery of six new susceptibility loci and analysis of pleiotropic effects in leprosy. Nature Genetics. 2015;47: 267–271.
doi:10.1038/ng.3212
6. Yang S-K, Hong M, Choi H, Zhao W, Jung Y, Haritunians T, et al. Immunochip Analysis Identification of 6 Additional Susceptibility Loci for Crohnʼs Disease in Koreans: Inflammatory Bowel Diseases. 2015;21: 1–7. doi:10.1097/MIB.0000000000000268
7. Ye BD, Choi H, Hong M, Yun WJ, Low H-Q, Haritunians T, et al. Identification of Ten Additional Susceptibility Loci for Ulcerative Colitis Through Immunochip Analysis in Koreans:
Inflammatory Bowel Diseases. 2016;22: 13–19. doi:10.1097/MIB.0000000000000584
8. Yang S-K, Hong M, Oh H, Low H-Q, Jung S, Ahn S, et al. Identification of Loci at 1q21 and 16q23 That Affect Susceptibility to Inflammatory Bowel Disease in Koreans.
Gastroenterology. 2016;151: 1096-1099.e4. doi:10.1053/j.gastro.2016.08.025
9. Liu JZ, Sommeren van S, Huang H, et al. Association Analyses Identify 38 Susceptibility Loci for Inflammatory Bowel Disease and Highlight Shared Genetic Risk across Populations.
Nature Genetics. 2015;47:979–86. doi.org/10.1038/ng.3359.
10. Wang Z, Sun Y, Fu X, Yu G, Wang C, Bao F, et al. A large-scale genome-wide association and meta-analysis identified four novel susceptibility loci for leprosy. Nature Communications.
2016;7: 13760. doi:10.1038/ncomms13760
11. Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nature Genetics. 2007;39: 906–913.
doi:10.1038/ng2088
12. Howie BN, Donnelly P, Marchini J. A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies. Schork NJ, editor. PLoS Genetics.
2009;5: e1000529. doi:10.1371/journal.pgen.1000529
13. The Wellcome Trust Case Control Consortium, Liu JZ, Tozzi F, Waterworth DM, Pillai SG, Muglia P, et al. Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nature Genetics. 2010;42: 436–440. doi:10.1038/ng.572
14. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: A Tool for Genome-wide Complex Trait Analysis. The American Journal of Human Genetics. 2011;88: 76–82.
doi:10.1016/j.ajhg.2010.11.011
15. Genetic Investigation of ANthropometric Traits (GIANT) Consortium, DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Yang J, Ferreira T, Morris AP, Medland SE, et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nature Genetics. 2012;44: 369–375.
doi:10.1038/ng.2213
16. Pickrell JK, Berisa T, Liu JZ, Ségurel L, Tung JY, Hinds DA. Detection and interpretation of shared genetic influences on 42 human traits. Nature Genetics. 2016;48: 709–717.
doi:10.1038/ng.3570
17. Bulik-Sullivan BK, Loh P-R, Finucane HK, Ripke S, Yang J, Patterson N, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.
Nature Genetics. 2015;47: 291–295. doi:10.1038/ng.3211
18. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Consortium R, et al. An Atlas of Genetic Correlations across Human Diseases and Traits. bioRxiv. 2015; doi:10.1101/014498 19. Lonsdale J. The Genotype-Tissue Expression (GTEx) project. nature genetics. 2013;45: 6.
30
20. The GTEx Consortium, Ardlie KG, Deluca DS, Segre AV, Sullivan TJ, Young TR, et al. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans.
Science. 2015;348: 648–660. doi:10.1126/science.1262110
21. Lamparter D, Marbach D, Rueedi R, Kutalik Z, Bergmann S. Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics. Listgarten J, editor. PLOS Computational Biology. 2016;12: e1004714. doi:10.1371/journal.pcbi.1004714 22. Cader MZ, Boroviak K, Zhang Q, Assadi G, Kempster SL, Sewell GW, et al. C13orf31 (FAMIN) is a central regulator of immunometabolic function. Nature Immunology. 2016;17:
1046–1056. doi:10.1038/ni.3532
23. Nishimura D. BioCarta. Biotech Software & Internet Report. 2001;2: 117–120.
doi:10.1089/152791601750294344
24. Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, Tanabe M. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Research. 2014;42: D199–
D205. doi:10.1093/nar/gkt1076
25. Croft D, O’Kelly G, Wu G, Haw R, Gillespie M, Matthews L, et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Research. 2011;39: D691–D697.
doi:10.1093/nar/gkq1018
26. Dinarello CA. Proinflammatory Cytokines. Chest. 2000;118: 503–508.
doi:10.1378/chest.118.2.503
27. LIU J, LIN A. Role of JNK activation in apoptosis: A double-edged sword. Cell Research.
2005; 7.
28. Lahiri A, Hedl M, Yan J, Abraham C. Human LACC1 increases innate receptor-induced responses and a LACC1 disease-risk variant modulates these outcomes. Nature Communications.
2017;8: 15614.
29. Takeuchi M, Mizuki N, Meguro A, Ombrello MJ, Kirino Y, Satorius C, et al. Dense genotyping of immune-related loci implicates host responses to microbial exposure in Behçet’s disease susceptibility. Nature Genetics. 2017; doi:10.1038/ng.3786
30. Hommes D, Van Den Blink B, Plasse T, Bartelsman J, Xu C, Macpherson B, et al. Inhibition of stress-activated MAP kinases induces clinical improvement in moderate to severe Crohn’s disease. Gastroenterology. 2002;122: 7–14. doi:10.1053/gast.2002.30770