ACPA-Negative and ACPA-Positive RA Share Susceptibility Loci
This study was designed in accordance with the Helsinki Declaration. This study was approved by the local ethics committees, namely, Kyoto University Graduate School and Faculty of Medicine, Ethics Committee and Ethics Committees of RIKEN, Tokyo Women's Medical University Matsuyama Red Cross Hospital, Keio University School of Medicine, Dohgo Spa Hospital, Niigata Rheumatic Center, Higashihiroshima Memorial Hospital, Kurashiki Sweet Hospital and Pharm C, Matsubara Mayflower Hospital. Written informed consent was obtained from all study participants. All data were de-identified and analyzed anonymously.
A total of 670 patients with ACPA-negative RA and 16,891 controls were enrolled in the three GWAS from RIKEN, Kyoto University, and Tokyo Women's Medical University, respectively, and 916 cases and 3,764 controls in the replication study. The subjects in GWAS were included in the meta-analysis of RA recently reported from GARNET consortium (RA meta-analysis hereafter). A summary of the participants is presented in Table 1. All of the patients fulfilled American College of Rheumatology (ACR) revised criteria for RA in 1987 or ACR and European League Against Rheumatism (EULAR) classification criteria for RA in 2010.
The MESACUP CCP ELISA kit (Medical and Biological Laboratories Co., Ltd, Nagoya, Japan) was used to detect second-generation ACPA in each RA patient, according to the manufacturer's instructions. A cutoff value of 4.5 U/ml was used to define ACPA positivity.
The serum RF concentrations of samples were quantified using a latex agglutination turbidimetric immunoassay or an ELISA assay. When multiple values for RF had been obtained at different visits, we used the maximum RF value for each patient. The cutoff values of each detection kit in each hospital were employed.
Microarrays in Illumina Infinium and Affymetrix were used for the meta-analysis of the three GWAS. Detailed information on the arrays was given in the previous report. In the replication study, Taqman assays were performed for genotyping case subjects, and genotype data for controls were extracted from array data of Illumina Infinium HumanHap610-Quad or HumanHap550 (Table 1). Association data for 2,822 patients with ACPA-positive RA and 16,891 controls were obtained from the RA meta-analysis. We applied the same quality-control criteria as the RA meta-analysis including call rate, minor allele frequency, and Hardy-Weinberg disequilibrium: these details were presented in the previous manuscript.
Imputed genotype data of ACPA-negative RA patients was extracted from the RA meta-analysis in a Japanese population. Briefly, MACH version 1.0.16 software was used for imputation of genotype data obtained by the GWAS of RA with the Hapmap phase II East Asian panel (JPT and CHB) as reference. As the meta-analysis in the current study included three different GWAS, the imputation was performed separately for data from each GWAS using the same reference panel. A total of 1,948,138 single nucleotide polymorphisms (SNPs) with minor allele frequency >1% and imputation score (Rsq) >0.5 were used for the analysis.
SNPs in the 13 regions that were reported to be associated with ACPA-negative RA in a European population were extracted from the GWAS meta-analysis. The 13 SNPs that had the strongest associations among the 13 regions were selected as representatives of the regions. We performed a total of 20,000 permutation tests to evaluate empirical P-values to obtain the smallest P-values <0.01 from 4 of the 13 regions.
SNPs with P-values <1 × 10 in the GWAS meta-analysis and contained in both the Illumina Infinium HumanHap 610-Quad array and the Human Hap 550 array, and for which real-time PCR primers and probes were successfully designed, were selected for the replication study. Rs3889769 was excluded due to difficulty in designing probes for the replication study. The 21 SNPs that were shown to be susceptibility markers in the RA meta-analysis and were contained in both the Illumina Infinium HumanHap 610-Quad array and the Human Hap 550 array were also selected for the replication study to analyze correlation of effect sizes (odds ratio, OR) between ACPA-negative and -positive RA. For FCRL3, rs17727339, which had a strong association with ACPA-negative RA, was used in the analysis.
Heterogeneity among three GWAS or among the GWAS and the replication study was evaluated by the Cochran Q-test and I.
Effect sizes (ORs) of risk alleles in the 21 SNPs were compared between ACPA-positive and -negative RA by calculating the Spearman correlation coefficient in the GWAS meta-analysis and the replication study. Correlation of effect sizes for non-HLA SNPs in GWAS meta-analysis was analyzed between ACPA-positive and -negative RA, -negative RF-positive or -negative RF-negative RA by Spearman's correlation coefficient for SNPs pruned by r <0.3 by PLINK with intervals of P-values. Data for r was obtained in the Hapmap phase II JPT data. HLA was defined from 25 Mbp to 35 Mbp on chromosome 6 based on NCBI build 36.
Dosage of risk alleles were assessed for associations with susceptibility to ACPA-negative RA by logistic regression analysis. The inverse-variance method was used to combine results of the three GWAS in the meta-analysis assuming a fixed-effect model from study-specific effect sizes (logarithm of ORs) and to combine results of the GWAS meta-analysis and the replication study. The QQ plot was used to assess population structure in the GWAS meta-analysis. Genomic control methods were applied to the test statistics in each of the three GWAS of ACPA-negative RA patients based on the inflation factor calculated in each study. Because the meta-analysis of the three GWAS did not show an inflation factor >1.0, we did not apply genomic control to the results of the meta-analysis. We also performed GWAS meta-analysis using age and sex as covariates. Statistical analyses were performed by R software or PLINK version 1.07.P-values <0.05 and 5 × 10 were regarded as significant for correlation analyses and GWAS, respectively.
Methods
Ethics Statement
This study was designed in accordance with the Helsinki Declaration. This study was approved by the local ethics committees, namely, Kyoto University Graduate School and Faculty of Medicine, Ethics Committee and Ethics Committees of RIKEN, Tokyo Women's Medical University Matsuyama Red Cross Hospital, Keio University School of Medicine, Dohgo Spa Hospital, Niigata Rheumatic Center, Higashihiroshima Memorial Hospital, Kurashiki Sweet Hospital and Pharm C, Matsubara Mayflower Hospital. Written informed consent was obtained from all study participants. All data were de-identified and analyzed anonymously.
Study Subjects
A total of 670 patients with ACPA-negative RA and 16,891 controls were enrolled in the three GWAS from RIKEN, Kyoto University, and Tokyo Women's Medical University, respectively, and 916 cases and 3,764 controls in the replication study. The subjects in GWAS were included in the meta-analysis of RA recently reported from GARNET consortium (RA meta-analysis hereafter). A summary of the participants is presented in Table 1. All of the patients fulfilled American College of Rheumatology (ACR) revised criteria for RA in 1987 or ACR and European League Against Rheumatism (EULAR) classification criteria for RA in 2010.
ACPA Detection
The MESACUP CCP ELISA kit (Medical and Biological Laboratories Co., Ltd, Nagoya, Japan) was used to detect second-generation ACPA in each RA patient, according to the manufacturer's instructions. A cutoff value of 4.5 U/ml was used to define ACPA positivity.
RF Detection
The serum RF concentrations of samples were quantified using a latex agglutination turbidimetric immunoassay or an ELISA assay. When multiple values for RF had been obtained at different visits, we used the maximum RF value for each patient. The cutoff values of each detection kit in each hospital were employed.
Genotyping
Microarrays in Illumina Infinium and Affymetrix were used for the meta-analysis of the three GWAS. Detailed information on the arrays was given in the previous report. In the replication study, Taqman assays were performed for genotyping case subjects, and genotype data for controls were extracted from array data of Illumina Infinium HumanHap610-Quad or HumanHap550 (Table 1). Association data for 2,822 patients with ACPA-positive RA and 16,891 controls were obtained from the RA meta-analysis. We applied the same quality-control criteria as the RA meta-analysis including call rate, minor allele frequency, and Hardy-Weinberg disequilibrium: these details were presented in the previous manuscript.
Imputation
Imputed genotype data of ACPA-negative RA patients was extracted from the RA meta-analysis in a Japanese population. Briefly, MACH version 1.0.16 software was used for imputation of genotype data obtained by the GWAS of RA with the Hapmap phase II East Asian panel (JPT and CHB) as reference. As the meta-analysis in the current study included three different GWAS, the imputation was performed separately for data from each GWAS using the same reference panel. A total of 1,948,138 single nucleotide polymorphisms (SNPs) with minor allele frequency >1% and imputation score (Rsq) >0.5 were used for the analysis.
Thirteen Regions Associated With ACPA-negative RA in a European Population
SNPs in the 13 regions that were reported to be associated with ACPA-negative RA in a European population were extracted from the GWAS meta-analysis. The 13 SNPs that had the strongest associations among the 13 regions were selected as representatives of the regions. We performed a total of 20,000 permutation tests to evaluate empirical P-values to obtain the smallest P-values <0.01 from 4 of the 13 regions.
SNP Selection for the Replication Study
SNPs with P-values <1 × 10 in the GWAS meta-analysis and contained in both the Illumina Infinium HumanHap 610-Quad array and the Human Hap 550 array, and for which real-time PCR primers and probes were successfully designed, were selected for the replication study. Rs3889769 was excluded due to difficulty in designing probes for the replication study. The 21 SNPs that were shown to be susceptibility markers in the RA meta-analysis and were contained in both the Illumina Infinium HumanHap 610-Quad array and the Human Hap 550 array were also selected for the replication study to analyze correlation of effect sizes (odds ratio, OR) between ACPA-negative and -positive RA. For FCRL3, rs17727339, which had a strong association with ACPA-negative RA, was used in the analysis.
Assessment of Heterogeneity
Heterogeneity among three GWAS or among the GWAS and the replication study was evaluated by the Cochran Q-test and I.
Correlation Analysis
Effect sizes (ORs) of risk alleles in the 21 SNPs were compared between ACPA-positive and -negative RA by calculating the Spearman correlation coefficient in the GWAS meta-analysis and the replication study. Correlation of effect sizes for non-HLA SNPs in GWAS meta-analysis was analyzed between ACPA-positive and -negative RA, -negative RF-positive or -negative RF-negative RA by Spearman's correlation coefficient for SNPs pruned by r <0.3 by PLINK with intervals of P-values. Data for r was obtained in the Hapmap phase II JPT data. HLA was defined from 25 Mbp to 35 Mbp on chromosome 6 based on NCBI build 36.
Statistical Analysis
Dosage of risk alleles were assessed for associations with susceptibility to ACPA-negative RA by logistic regression analysis. The inverse-variance method was used to combine results of the three GWAS in the meta-analysis assuming a fixed-effect model from study-specific effect sizes (logarithm of ORs) and to combine results of the GWAS meta-analysis and the replication study. The QQ plot was used to assess population structure in the GWAS meta-analysis. Genomic control methods were applied to the test statistics in each of the three GWAS of ACPA-negative RA patients based on the inflation factor calculated in each study. Because the meta-analysis of the three GWAS did not show an inflation factor >1.0, we did not apply genomic control to the results of the meta-analysis. We also performed GWAS meta-analysis using age and sex as covariates. Statistical analyses were performed by R software or PLINK version 1.07.P-values <0.05 and 5 × 10 were regarded as significant for correlation analyses and GWAS, respectively.