정슬기 이학석사학위 한국을 인준함. Previous genome-wide association studies performed in Chinese patients with leprosy and in European patients with Crohn's disease (CD) suggested that CD and leprosy may share a common underlying genetic susceptibility related to nonspecific innate immunity. There are no genome-wide comparisons between CD and leprosy susceptibility in Asian populations.
We performed a CD meta-analysis containing 3,566 cases and 8,885 controls of Korean, Chinese, and Japanese ancestry, and a leprosy meta-analysis containing 2,960 cases and 3,747 controls of Chinese ancestry. We then compared susceptibility loci with CD and leprosy for identification of common susceptibility loci for both diseases. Of the 9 common loci, only 2 loci, including LACC1at 13q14.1 and RIPK2 at 8q21.3, showed concordance in allelic effects between CD and leprosy.
Our study showed that CD and leprosy shared about half of its genetic susceptibility in East Asians. Most of the common CD and leprosy susceptibility loci showed allelic effects in the opposite direction, suggesting their role in both defense against infection and autoimmunity.
INTRODUCTION
MATERIALS AND METHODS
- Study subjects of Crohn’s disease datasets
- Study subjects of leprosy datasets
- Quality controls
- Imputation
- Meta-analysis
- Posterior probability calculation
- Genetic correlation
- Expression quantitative trait loci
- Pathway analysis
The quality control (QC) procedures of each CD and leprosy dataset were described in previous studies. Pooled statistics of 4 datasets were used for CD meta-analysis and 3 independent GWAS datasets for leprosy meta-analysis (Table 1). The MHC region (chromosome Mb) was excluded from the comparative study between CD and leprosy because of its unusual genetic architecture and differences in the LD pattern between East Asian populations.
Finally, the number of available SNPs was 75,649 SNPs in the East Asian CD meta-analysis and 5,498,605 SNPs in the Chinese leprosy meta-analysis. The CD susceptibility locus in the current meta-analysis was defined by SNPs with r2>0.4 or the region flanking 250 kb on either side of the major SNP at P<5 x 10-8. After a meta-analysis of conditional datasets, SNPs with a conditional P value less than the genome-wide significance level were considered as independent signals.
Although the sample size of the current meta-analysis is similar to those of the discovery stage of the previous studies, 13 more loci were reported due to almost three times more of the discovery sample size of replication stages 1–5,10. For a comparison between CD and leprosy susceptibility loci, all genetic loci including SNPs that achieved genome-wide significance in the current meta-analysis or previously reported SNPs were considered susceptibility loci for CD or leprosy. When the leprosy driver SNP was different from previous study and present meta-analysis in common susceptibility loci for CD and leprosy, we used SNPs in higher LD with CD susceptibility SNP (Table 4).
To assess the strength of the association between CD and leprosy, we used a significance level of P <2.38 x where 21 is the number of susceptible SNPs identified in CD or leprosy. To identify the genomic region with the causative variant of CD and leprosy, we estimate the posterior probability of 4 different models in each genomic region using gwas-pw16. Data on the detected genomic region were available on the NY Genome Center research site (https://bitbucket.org/nygcresearch/ldetect-data/downloads).
We used 70,323 common SNPs from CD and leprosy summary statistics as input file of gwas-pw. We used the East Asian data (JPT + CHB, Phase 3) from the 1,000 Genomes Project databases (http://www. 1000genomes.org/data) as LD reference panels for both CD and leprosy. Summary statistics of CD and leprosy meta-analysis result of common 35,016 SNPs were used for input data.
We used summary statistics of the CD and leprosy meta-analysis results (75,649 SNPs in CD and 5,498,605 SNPs in leprosy) as input files and the East Asian data (JPT + CHB, Phase 3) from the 1,000 Genomes Project databases as LD reference panel. For the gene scoring method, sum-of-chi-square (SOCS) was chosen to combine the results for all SNPs in the gene region.
RESULTS
- Meta-analysis
- Common susceptibility loci to Crohn’s disease and leprosy
- Comparison of allelic effect direction between Crohn’s disease and leprosy · 15
- Pathway analysis
Regional association depicts Crohn's disease meta-analysis associations in the upper panel and leprosy meta-analysis associations in the lower panel. The yellow diamond presented a CD lead SNP in the upper panel and a leprosy SNP in the lower panel of the plot. In total, we identified 9 common CD and leprosy susceptibility loci that reach genome-wide significance or the Bonferroni-corrected threshold.
The LD data between each independent SNP of CD and leprosy in the 9 common susceptibility loci were shown in Table 7. To study a total of 9 common susceptibility loci for CD and leprosy in a different way, we used gwas-pw to estimate the posterior probability of model 3, that a particular genomic region contains a genetic variant that affects both CD and leprosy. Among the 9 common susceptibility loci, there was no locus specifically susceptible to CD or leprosy, suggesting that these 9 loci may be associated with CD and leprosy.
To estimate linear correlation between allelic effects of CD and leprosy, we calculated a Pearson correlation coefficient using odds ratios of 7 CD susceptibility SNPs meeting Bonferroni-corrected threshold in leprosy meta-analysis result (Pearson s r = -0.77, P-value = 0.04) (Fig. 2.A). Pearson correlation coefficients showed that allelic effects of CD and leprosy were in negative linear correlation. A genetic correlation estimated by LDSC v1.0.0 using meta-analysis result of CD and leprosy also showed negative directions (-0.61) with significant P value.
To examine whether the associated variants are involved in differential regulation of gene expression, eQTL analysis within 9 common susceptibility loci for CD and leprosy was performed using the GTEx database. Among multiple genes with eQTL effects at each locus, we focused on genes corresponding to the same or opposite direction of the effects in the comparison between CD and leprosy. Rs3764147 (A>G), a missense variant leading to amino acid substitution of isoleucine at position 254 to valine, was found to be associated with macrophage metabolic function.22 Both the CD risk allele of rs61960013 and the leprosy risk allele of rs3764147 were associated with lower expression of LACC1 and CCDC122 in lymphocytes and in nervous tissue.
Leprosy risk allele of rs2058660 was associated with lower expression of IL18R1 in skin tissue but higher expression of IL18RAP and AC007278.3 in whole blood. The CD risk allele of rs4366152 was associated with lower expression and the leprosy risk allele of rs6478108 with higher expression of TNFSF15. Leprosy risk allele was associated with higher expression of AP003774.1, lower expression of CCDC88B and PPP1R14B in the same tissues.
The expression of all three genes appeared to be regulated in the opposite direction by the main risk variants for celiac disease and leprosy in the two diseases. To identify relevant pathways in Crohn's disease and leprosy, we used PASCAL for SNP mapping in the Crohn's disease and leprosy meta-analysis to genes mapped to pathways in BIOCARTA,23 KEGG,24 and REACTOME. 25From a total of 1,077 routes listed according to empirical P-values, we presented top 10 routes in Table 10.
DISCUSSION
Identification of common susceptibility loci between CD and leprosy can be accelerated using a high-density SNP array with uniform genome coverage. Our study showed that 7 out of 9 common susceptibility loci for CD and leprosy have an opposite genetic effect. Only two susceptibility loci (LACC1, RIPK2 loci) showed consistent associations between CD and leprosy, consistent with published data.5 LACC1 determined mitochondrial and nicotinamide adenine dinucleotide phosphate oxidase-dependent reactive oxygen species production, bactericidal activity, and inflammasome activation in macrophages .19 Loss-of-function for the LACC1p.Ile254Val (rs3764147) variant reduced the function of microbial recognition and host pattern recognition receptor-mediated responses, potentially leading to dysfunction of cytokines and bacterial clearance. 28 It is interesting to see that LACC1rs2121033 (high LD with rs3764147 p.Ile254Val; r2 = 0.94) confers protection against Behçet's disease, in contrast to CD and leprosy. 29 This is the first report in East Asians that CD is associated with NOD2 and RIPK2.
It is noteworthy that the microbial sensor NOD2 showed opposite genetic effects between CD and leprosy, while RIPK2 showed consistent effects between CD and leprosy. eQTL analyzes for CD and leprosy risk variants from 2 loci with concordant effects showed consistent expression patterns of RP11-37B2.1at 8q21.3 in skin and LACC1 and CCDC122 at 13q14.1 in lymphocytes and neural tissue. In the 7 common susceptibility loci with opposite effects in CD and leprosy, CD and leprosy lead risk variants showed opposite effects on the expression of several candidate genes among multiple genes at each locus.
In the leprosy pathway analysis, only 2 common susceptibility loci (RIPK2, NOD2) to CD and leprosy were included in the most important pathway (JNK. Since leprosy has two distinct clinical manifestations, defined as tuberculoid and lepromatous, it would be interesting to examine which type shares common genetic susceptibility loci with CD, or the specific subtype of CD.
Association Analyzes Identify 38 Susceptibility Loci for Inflammatory Bowel Disease and Highlight Shared Genetic Risk Across Populations. A genome-wide association and meta-analysis identified four novel leprosy susceptibility loci. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies.
Konzorcij Wellcome Trust Case Control, Liu JZ, Tozzi F, Waterworth DM, Pillai SG, Muglia P, et al. 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. Konzorcij GTEx, Ardlie KG, Deluca DS, Segre AV, Sullivan TJ, Young TR, et al.
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