ACPA-Negative and ACPA-Positive RA Share Susceptibility Loci
ACPA-Negative and ACPA-Positive RA Share Susceptibility Loci
Because the number of studies addressing susceptibility loci to ACPA-negative RA including candidate gene analyses is quite limited, this is the first report of GWAS addressing ACPA-negative RA in an Asian population and one of the largest in the world. Our study revealed that ACPA-negative RA shares a large proportion of susceptibility loci with ACPA-positive RA for non-HLA alleles. Moreover, our results suggest that ACPA-negative RA consists of two genetically distinct subsets based on RF positivity even for non-HLA genes. Our study participants showed 45.5% of positivity of RF, compatible to the previous ACPA-negative RA cohorts.
We confirmed that inclusion of age and sex as covariates did not alter the results. This means that as the prevalence of RA is around 1% and the controls are not young, the influence of contamination of subjects among controls who develop RA in the future should be very small.
Our study confirmed an association between ACPA-negative RA and the HLA locus and revealed suggestive associations of FCRL3 on chromosome 1 and CSMD1 on chromosome 8. We did not find heterogeneity among the studies, indicating that these associations were not obtained by one or two studies with extreme association. While the female ratio was different between cases and controls, we did not adjust for sex. This is because most of the previous GWAS, including our previous meta-analysis, did not adjust for sex to analyze autosomal SNPs.
Because rs6904716 in the HLA locus was not very strongly associated with ACPA-positive RA in the RA meta-analysis, these results suggest that associated HLA alleles are different between ACPA-positive and ACPA-negative RA. Low effect sizes of the HLA locus to ACPA-negative RA may explain the lack of significant association of the HLA locus in the meta-analysis of GWAS.
FCRL3 has been reported to be associated with RA, especially in the Japanese population. Rs7528684 is considered to be the causative variant of the FCRL3 region to which NFκB binds and activates B cells to produce antibody by augmenting BCR-mediating signals. Rs17727339 is in moderate linkage disequilibrium (LD) with rs7528684 (D':0.80 and r:0.28). Although the association of rs17727339 in the combined study did not reach the GWAS significant level, the replication study supported the association of FCRL3 with ACPA-negative RA. Thus, it is likely that the FCRL3 region, possibly as a consequence of association of rs7528684, is associated with both ACPA-positive and -negative RA.
CSMD1 is a tumor-suppressor gene associated with psoriasis, Kawasaki disease and schizophrenia.CSMD1 expresses mainly in epithelial cells and exhibits anti-tumor activity through activation of the Smad pathway.CSMD1 also functions as a complement regulatory gene, especially of the classical pathway. Thus, the role of CSMD1 on complement or other genes nearby CSMD1 may have an important role on the development of ACPA-negative RA. This chromosome 8 region was not associated with ACPA-positive RA in the RA meta-analysis (P = 0.87), therefore, this region may be an ACPA-negative RA-specific associated region. Considering the moderate heterogeneity of rs6986423, further replication studies would confirm the association between this region and ACPA-negative RA.
Four regions out of the thirteen genes that had been shown to be associated with ACPA-negative RA in European populations had P-values <0.01 (Additional file 3 http://www.arthritis-research.com/content/17/1/104/additional) in the Japanese population. Although these markers did not reach the stringent significance level, the current results suggest that ACPA-negative RA shares susceptibility loci beyond ethnicity.
Although we did not find ACPA-negative RA-associated genes with a GWAS-significance level due to the limited power of case subjects, the current results suggested that the majority of non-HLA susceptibility loci are shared between ACPA-positive and ACPA-negative RA. Correlation analysis of the 21 SNPs in the susceptibility loci to Japanese RA showed significant correlations of ORs between ACPA-positive and ACPA-negative RA in both the GWAS meta-analysis and the replication study. These consistent correlations support similarity of genetic background between the two RA subsets. Such results are consistent with the previous US report that compared the ORs of representative SNPs in 29 RA susceptibility loci of European ancestry between ACPA-positive RA and ACPA-negative RA. We also showed that the ORs for SNPs weakly associated with ACPA-positive RA are correlated with ACPA-negative RA, with the strength of correlation depending on the strength of the P-values for the association. While the UK study emphasized the categories of susceptibility loci (for example, some genes are associated only with ACPA-positive RA and some genes with both ACPA-positive and ACPA-negative RA), we assume that the majority of the susceptibility loci are shared between ACPA-positive and ACPA-negative RA. Because the results of susceptibility analysis for genes with a small effect size vary by the sample size, correlation analysis of effect size may be more powerful than the orthodox association analysis in such cases. In fact, when we calculated the correlation of ORs for the 35 non-HLA SNPs from the table in the UK study, the correlation coefficient was 0.63, showing that many of the genes are shared between ACPA-positive and -negative RA. These strong correlations between ACPA-positive and -negative RA matches with the current study as well as the US study.
On the contrary, HLA-association seems to be different between ACPA-positive and -negative RA. We and others have already shown that the HLA-DRB1 allele usage in ACPA-negative RA is different from that in ACPA-positive RA. The current study confirmed the previous results, including those of the UK studies, in that rs6904716, which had the smallest P-value for ACPA-negative RA in the current study, did not show strong association in ACPA-positive RA in the RA meta-analysis (P = 2.3 × 10) compared with other SNPs in the HLA locus (the smallest P = 1.2 × 10), whereas rs7764819, displaying the strongest association in the HLA locus with ACPA-positive RA in the RA meta-analysis, did not even show a suggestive association with ACPA-negative RA in the current study (P = 0.56). All these results confirmed the idea that ACPA-negative RA uses the different HLA allele from ACPA-positive RA. These results may suggest that T cells in ACPA-positive RA react against relatively uniform autoantigens, citrullinated proteins, whereas T cells in ACPA-negative RA react with varied autoanitigens.
In the current study, we also confirmed that the two subsets of ACPA-negative RA, ACPA-negative RF-positive RA and ACPA-negative RF-negative RA, are genetically distinct. Previously we reported that the HLA allele usage is different between the two ACPA-negative RA subsets. Here we showed that not only HLA allele usage but also the association of non-HLA genes is different (Figure 3C). As Figure 3A and B show, ACPA-negative RF-positive RA is genetically closer to ACPA-positive RA than ACPA-negative RF-negative RA. Therefore, only ACPA-negative RF-negative RA may be a relatively different subset from conventional RA including ACPA-negative RF-positive RA (see Additional file 8 http://www.arthritis-research.com/content/17/1/104/additional).
Because ACPA-negative RA represents a minor subset of RA, it is difficult to perform the association analysis to detect common variants with small effect sizes. In such cases, correlation analysis of effect size of each SNP may be more powerful to determine whether two different subsets share the majority of susceptibility loci or not. We assume that many of the susceptibility loci are shared except for the HLA allele between ACPA-positive and -negative RA, but worldwide meta-analysis would be necessary to confirm this idea.
Discussion
Because the number of studies addressing susceptibility loci to ACPA-negative RA including candidate gene analyses is quite limited, this is the first report of GWAS addressing ACPA-negative RA in an Asian population and one of the largest in the world. Our study revealed that ACPA-negative RA shares a large proportion of susceptibility loci with ACPA-positive RA for non-HLA alleles. Moreover, our results suggest that ACPA-negative RA consists of two genetically distinct subsets based on RF positivity even for non-HLA genes. Our study participants showed 45.5% of positivity of RF, compatible to the previous ACPA-negative RA cohorts.
We confirmed that inclusion of age and sex as covariates did not alter the results. This means that as the prevalence of RA is around 1% and the controls are not young, the influence of contamination of subjects among controls who develop RA in the future should be very small.
Our study confirmed an association between ACPA-negative RA and the HLA locus and revealed suggestive associations of FCRL3 on chromosome 1 and CSMD1 on chromosome 8. We did not find heterogeneity among the studies, indicating that these associations were not obtained by one or two studies with extreme association. While the female ratio was different between cases and controls, we did not adjust for sex. This is because most of the previous GWAS, including our previous meta-analysis, did not adjust for sex to analyze autosomal SNPs.
Because rs6904716 in the HLA locus was not very strongly associated with ACPA-positive RA in the RA meta-analysis, these results suggest that associated HLA alleles are different between ACPA-positive and ACPA-negative RA. Low effect sizes of the HLA locus to ACPA-negative RA may explain the lack of significant association of the HLA locus in the meta-analysis of GWAS.
FCRL3 has been reported to be associated with RA, especially in the Japanese population. Rs7528684 is considered to be the causative variant of the FCRL3 region to which NFκB binds and activates B cells to produce antibody by augmenting BCR-mediating signals. Rs17727339 is in moderate linkage disequilibrium (LD) with rs7528684 (D':0.80 and r:0.28). Although the association of rs17727339 in the combined study did not reach the GWAS significant level, the replication study supported the association of FCRL3 with ACPA-negative RA. Thus, it is likely that the FCRL3 region, possibly as a consequence of association of rs7528684, is associated with both ACPA-positive and -negative RA.
CSMD1 is a tumor-suppressor gene associated with psoriasis, Kawasaki disease and schizophrenia.CSMD1 expresses mainly in epithelial cells and exhibits anti-tumor activity through activation of the Smad pathway.CSMD1 also functions as a complement regulatory gene, especially of the classical pathway. Thus, the role of CSMD1 on complement or other genes nearby CSMD1 may have an important role on the development of ACPA-negative RA. This chromosome 8 region was not associated with ACPA-positive RA in the RA meta-analysis (P = 0.87), therefore, this region may be an ACPA-negative RA-specific associated region. Considering the moderate heterogeneity of rs6986423, further replication studies would confirm the association between this region and ACPA-negative RA.
Four regions out of the thirteen genes that had been shown to be associated with ACPA-negative RA in European populations had P-values <0.01 (Additional file 3 http://www.arthritis-research.com/content/17/1/104/additional) in the Japanese population. Although these markers did not reach the stringent significance level, the current results suggest that ACPA-negative RA shares susceptibility loci beyond ethnicity.
Although we did not find ACPA-negative RA-associated genes with a GWAS-significance level due to the limited power of case subjects, the current results suggested that the majority of non-HLA susceptibility loci are shared between ACPA-positive and ACPA-negative RA. Correlation analysis of the 21 SNPs in the susceptibility loci to Japanese RA showed significant correlations of ORs between ACPA-positive and ACPA-negative RA in both the GWAS meta-analysis and the replication study. These consistent correlations support similarity of genetic background between the two RA subsets. Such results are consistent with the previous US report that compared the ORs of representative SNPs in 29 RA susceptibility loci of European ancestry between ACPA-positive RA and ACPA-negative RA. We also showed that the ORs for SNPs weakly associated with ACPA-positive RA are correlated with ACPA-negative RA, with the strength of correlation depending on the strength of the P-values for the association. While the UK study emphasized the categories of susceptibility loci (for example, some genes are associated only with ACPA-positive RA and some genes with both ACPA-positive and ACPA-negative RA), we assume that the majority of the susceptibility loci are shared between ACPA-positive and ACPA-negative RA. Because the results of susceptibility analysis for genes with a small effect size vary by the sample size, correlation analysis of effect size may be more powerful than the orthodox association analysis in such cases. In fact, when we calculated the correlation of ORs for the 35 non-HLA SNPs from the table in the UK study, the correlation coefficient was 0.63, showing that many of the genes are shared between ACPA-positive and -negative RA. These strong correlations between ACPA-positive and -negative RA matches with the current study as well as the US study.
On the contrary, HLA-association seems to be different between ACPA-positive and -negative RA. We and others have already shown that the HLA-DRB1 allele usage in ACPA-negative RA is different from that in ACPA-positive RA. The current study confirmed the previous results, including those of the UK studies, in that rs6904716, which had the smallest P-value for ACPA-negative RA in the current study, did not show strong association in ACPA-positive RA in the RA meta-analysis (P = 2.3 × 10) compared with other SNPs in the HLA locus (the smallest P = 1.2 × 10), whereas rs7764819, displaying the strongest association in the HLA locus with ACPA-positive RA in the RA meta-analysis, did not even show a suggestive association with ACPA-negative RA in the current study (P = 0.56). All these results confirmed the idea that ACPA-negative RA uses the different HLA allele from ACPA-positive RA. These results may suggest that T cells in ACPA-positive RA react against relatively uniform autoantigens, citrullinated proteins, whereas T cells in ACPA-negative RA react with varied autoanitigens.
In the current study, we also confirmed that the two subsets of ACPA-negative RA, ACPA-negative RF-positive RA and ACPA-negative RF-negative RA, are genetically distinct. Previously we reported that the HLA allele usage is different between the two ACPA-negative RA subsets. Here we showed that not only HLA allele usage but also the association of non-HLA genes is different (Figure 3C). As Figure 3A and B show, ACPA-negative RF-positive RA is genetically closer to ACPA-positive RA than ACPA-negative RF-negative RA. Therefore, only ACPA-negative RF-negative RA may be a relatively different subset from conventional RA including ACPA-negative RF-positive RA (see Additional file 8 http://www.arthritis-research.com/content/17/1/104/additional).
Because ACPA-negative RA represents a minor subset of RA, it is difficult to perform the association analysis to detect common variants with small effect sizes. In such cases, correlation analysis of effect size of each SNP may be more powerful to determine whether two different subsets share the majority of susceptibility loci or not. We assume that many of the susceptibility loci are shared except for the HLA allele between ACPA-positive and -negative RA, but worldwide meta-analysis would be necessary to confirm this idea.
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