The Turkish journal of pediatrics · 2020
Choroid plexus papilloma in extragonadal teratoma with predominantly neurogenic elements: a case report and review of the literature.
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Chiaroni-Clarke Rachel C, Munro Jane E, Chavez Raul A, Pezic Angela, Allen Roger C, Akikusa Jonathan D, Piper Susan E, Saffery Richard, Ponsonby Anne-Louise, Ellis Justine A
Аннотация
BACKGROUND: The prevalence of adnexal masses is 0.17% to 5.9% in asymptomatic and 7.1% to 12% in symptomatic patients. They can be benign or malignant. The initial detection and evaluation of an adnexal mass requires a high index of suspicion, a thorough history and examination and careful attention to subtle historical clues. Timely appropriate laboratory studies like serum cancer markers and radiographic studies are required to make a diagnosis, and finally, histopathological report tells us the exact nature of mass and guides further management.
METHODOLOGY: This prospective observational study was conducted in the Department of Obstetrics and Gynaecology, SMGS Hospital, GMC Jammu, over a period of one year from August 1st, 2023 to July 31st, 2024. The aim of this study was to determine the prevalence of adnexal masses with their related factors in this age group and to evaluate the adnexal mass in perimenopausal and postmenopausal age group. A total of 100 patients of adnexal masses with age > 40 years meeting inclusion and exclusion were included in the study. From all patients, history was obtained, clinical examination done followed by blood analysis, tumor markers, ultrasonography findings, CT and MRI findings of pelvic organs wherever applicable was done. Histopathological report was obtained.
RESULTS: Adnexal mass was found to be more common in age group 40-50 years (48%). The usual presentation was with symptoms of abdominal pain (76%). Left-sided ovarian mass (41%) was more common than right-sided (32%) or bilateral masses (13%). USG 1 score was reported mostly in subjects with benign mass (67 out of 71), while USG 4 score was reported maximum among malignant mass (19 out of 29). CA-125 < 35 level was reported maximum among benign mass (68 out of 72), while > 35 level of CA-125 was reported maximum among malignant mass (19 out of 28). RMI < 200 was found mostly among benign mass (77 out of 79), while RMI > 200 was reported maximum in malignant mass (21 out of 21). In our study, each parameter when compared with histopathological report using kappa stats, results were found to be statistically significant (p < 0.05). Benign masses were present in 64.2% cases and malignant masses were present in 35.8% cases in perimenopausal age group and 33.3% benign and 66.7% malignant cases were present in postmenopausal age group.
CONCLUSION: Ultrasound and CA-125 are important adjuvants which play vital role in the diagnosis and treatment of adnexal masses in perimenopausal and post-menopausal women. RMI and histopathology findings are in positive correlation. Therefore, it can be concluded that RMI can be used for evaluation of adnexal mass preoperatively and thus guides further management.
METHODOLOGY: This prospective observational study was conducted in the Department of Obstetrics and Gynaecology, SMGS Hospital, GMC Jammu, over a period of one year from August 1st, 2023 to July 31st, 2024. The aim of this study was to determine the prevalence of adnexal masses with their related factors in this age group and to evaluate the adnexal mass in perimenopausal and postmenopausal age group. A total of 100 patients of adnexal masses with age > 40 years meeting inclusion and exclusion were included in the study. From all patients, history was obtained, clinical examination done followed by blood analysis, tumor markers, ultrasonography findings, CT and MRI findings of pelvic organs wherever applicable was done. Histopathological report was obtained.
RESULTS: Adnexal mass was found to be more common in age group 40-50 years (48%). The usual presentation was with symptoms of abdominal pain (76%). Left-sided ovarian mass (41%) was more common than right-sided (32%) or bilateral masses (13%). USG 1 score was reported mostly in subjects with benign mass (67 out of 71), while USG 4 score was reported maximum among malignant mass (19 out of 29). CA-125 < 35 level was reported maximum among benign mass (68 out of 72), while > 35 level of CA-125 was reported maximum among malignant mass (19 out of 28). RMI < 200 was found mostly among benign mass (77 out of 79), while RMI > 200 was reported maximum in malignant mass (21 out of 21). In our study, each parameter when compared with histopathological report using kappa stats, results were found to be statistically significant (p < 0.05). Benign masses were present in 64.2% cases and malignant masses were present in 35.8% cases in perimenopausal age group and 33.3% benign and 66.7% malignant cases were present in postmenopausal age group.
CONCLUSION: Ultrasound and CA-125 are important adjuvants which play vital role in the diagnosis and treatment of adnexal masses in perimenopausal and post-menopausal women. RMI and histopathology findings are in positive correlation. Therefore, it can be concluded that RMI can be used for evaluation of adnexal mass preoperatively and thus guides further management.
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.
Findings
Juvenile Idiopathic Arthritis (JIA) is the most common rheumatic disease in children [ 1 ], yet there are relatively few confirmed disease associated loci, and we are therefore limited in our understanding of the overall genetic architecture [ 2 , 3 ]. Only two genome-wide association studies have been published for JIA [ 4 , 5 ], limited by sample size and/or genomic coverage. In these studies, a discovery sample was used to identify potential risk loci, and then an independent replication sample was used to confirm the findings. In both studies, a number of loci were identified as associated with JIA in the discovery sample that were unable to be replicated in the validation sample [ 4 , 5 ]. This highlights the importance of independent replication in determining true disease associated loci.
In 2013, Hinks et al. published the most successful genetic association study for JIA to date [ 6 ], using the Immunochip [ 7 ]; a genotyping array specifically designed to densely capture variation at immune-related genetic loci. The study further validated the well-established associations of HLA, PTPN22 and PTPN2 with JIA, in addition to confirming the involvement of other loci such as STAT4 with JIA, that were previously associated with the disease, but not at a genome-wide level of significance (p < 5 × 10 -8 )[ 6 ]. More notably, the same study identified a suite of novel JIA risk loci reaching either genome-wide significance, or a genome-wide ‘suggestive’ level of significance (p < 1 × 10 -6 ) [ 6 ]. However, in order to maximize statistical power for discovery, all available JIA cases formed the discovery sample, and no replication sample was available to confirm these novel associations. Therefore, we aimed to confirm the involvement of these new JIA risk loci in an independent sample of JIA cases and healthy child controls drawn from the CLARITY JIA case–control study in Melbourne Australia [ 8 , 9 ].
We selected 22 single nucleotide polymorphisms (SNPs) from the JIA Immunochip association study [ 6 ], for potential replication. SNPs that are established susceptibility loci for JIA [ 4 ], or that have previously been associated in our sample [ 8 ] (either the SNP itself or a SNP in high linkage disequilibrium) were omitted. Two SNPs selected for inclusion subsequently failed assay design and were therefore also excluded. Reasons for exclusion are listed in Additional file 1: Table S1. Genotyping of the remaining 20 SNPs was attempted in a total of 408 JIA cases and 687 controls drawn from CLARITY, using the Sequenom MassARRAY system (assay design details available from the authors). Following exclusion of individuals and SNPs not meeting quality control thresholds (<90% call rate for individuals or SNPs, and departure from Hardy Weinberg equilibrium p < 0.001), genotype data was successfully generated for 404 JIA cases (mean age 6.4 years, 68% female) and 676 healthy child controls (mean age 7.1 years, 42% female) across 19 SNPs (listed in Table 1 ). One SNP, rs66718203 at PRM1-RM12 , failed genotyping quality control and was excluded from further analysis. We re-genotyped a random 10% selection of samples across all SNPs and achieved a genotype concordance rate of >99%. Allelic, genotypic, additive (Cochrane-Armitage test for trend), dominant and recessive association tests were performed using PLINK v1.07 [ 10 ]. We considered p < 0.05 for any test, along with an allelic odds ratio (OR) in a direction consistent with the previous report, as evidence of replication. As these SNPs had a priori evidence for association, we did not adjust the p values for multiple testing.
Table 1
CLARITY association replication results
Gene SNP Minor allele Case MAF/Control MAF Allelic Best Immunochip Replication
c
P OR (95% CI) P
a
P
b
OR (95% CI)
13q14
rs34132030 T 0.30/0.30 0.82 1.02 (0.85-1.24) 0.68 D 1.77 × 10 -7 A 1.18(1.11-1.26) N
AFF3-LONRF2
rs6740838 T 0.44/0.41 0.25 1.11 (0.93-1.32) 0.042 R 8.83 × 10 -7 D 1.25(1.14-1.37) Y
ANKRD55
rs71624119 A 0.20/0.23 0.10 0.83 (0.67-1.03) 0.081 R 4.40 × 10 -11 A 0.78(0.73-0.84) S
ATP8B2-IL6R
rs11265608 A 0.08/0.07 0.50 1.12 (0.81-1.55) 0.50 T 2.75 × 10 -8 D 1.33(1.20-1.47) N
C5orf56-IRF1
rs4705862 T 0.42/0.45 0.11 0.87 (0.73-1.04) 0.033 D 1.02 × 10 -8 A 0.84(0.79-0.89) Y
CCR1-CCR3
rs79893749 T 0.09/0.11 0.10 0.78 (0.58-1.05) 0.10 A 1.88 × 10 -7 A 0.78(0.72-0.86) S
ERAP2-LNPEP
rs27290 G 0.47/0.39 0.00021 1.40 (1.17-1.66) 0.00021 A 7.50 × 10 -9 D 1.32(1.20-1.45) Y
FAS
rs7069750 G 0.47/0.45 0.19 1.12 (0.94-1.34) 0.19 A 2.93 × 10 -8 A 1.18(1.11-1.25) S
IL2-IL21
rs1479924 G 0.27/0.28 0.54 0.94 (0.77-1.15) 0.16 R 6.24 × 10 -11 A 0.79(0.74-0.85) S
IL2RB
rs2284033 A 0.40/0.43 0.24 0.90 (0.75-1.08) 0.18 D 1.55 × 10 -8 A 0.84(0.79-0.89) S
IL6
rs7808122 T 0.41/0.40 0.76 1.03 (0.86-1.23) 0.59 R 5.80 × 10 -8 A 1.19(1.11-1.25) N
JAZF1
rs10280937 C 0.15/0.14 0.77 1.04 (0.81-1.33) 0.74 D 6.60 × 10 -7 A 1.25(1.15-1.37) N
LTBR
rs2364480 C 0.26/0.27 0.65 0.96 (0.78-1.16) 0.49 R 5.10 × 10 -8 A 1.20(1.12-1.28) N
PRR5L
rs4755450 A 0.34/0.38 0.043 0.83 (0.69-0.99) 0.042 D 3.35 × 10 -7 D 0.80(0.74-0.87) Y
RUNX1
rs9979383 C 0.32/0.37 0.013 0.79 (0.66-0.95) 0.013 A 1.06 × 10 -8 D 0.78(0.72-0.85) Y
RUNX3
rs4648881 A 0.48/0.50 0.37 0.92 (0.78-1.10) 0.19 D 4.66 × 10 -7 A 1.16(1.10-1.23) S*
TYK2
rs34536443 C 0.02/0.03 0.10 0.63 (0.36-1.10) 0.10 A 1.00 × 10 -10 A 0.56(0.47-0.67) S
UBE2L3
rs2266959 T 0.23/0.19 0.040 1.25 (1.01-1.55) 0.036 T 6.20 × 10 -9 D 1.24(1.15-1.33) Y
ZFP36L1
rs12434551 T 0.46/0.45 0.61 1.05 (0.88-1.25) 0.50 R 1.59 × 10 -8 D 0.77(0.71-0.85) N
a Model providing most significant P value: A = allelic, D = dominant, G = genotypic, R = recessive, T = trend (Cochrane-Armitage Trend Test, also referred to as additive).
b Immunochip model: A = additive, D = dominant.
c Evidence for replication Y = yes N = no S = suggestive * = opposite direction of effect due to the minor allele being opposite between the two studies.
Bold = reached genome-wide significance in Immunochip paper.
CLARITY association replication results
a Model providing most significant P value: A = allelic, D = dominant, G = genotypic, R = recessive, T = trend (Cochrane-Armitage Trend Test, also referred to as additive).
b Immunochip model: A = additive, D = dominant.
c Evidence for replication Y = yes N = no S = suggestive * = opposite direction of effect due to the minor allele being opposite between the two studies.
Bold = reached genome-wide significance in Immunochip paper.
Table 1 shows the ORs and p values for allelic association tests, as well as the ‘best test’ model and corresponding p value. These are shown alongside the reported ORs, p value, and model from the Immunochip study. Figure 1 shows a forest plot of the allelic odds ratios generated in the CLARITY sample compared to the Immunochip study ORs for all SNPs tested. We report replication of association for six genes (SNPs) in our sample - AFF3-LONRF2 (rs6740838), C5orf56-IRF1 (rs4705862), ERAP2-LNPEP (rs27290), PRR5L (rs4755450), RUNX1 (rs9979383) and UBE2L3 (rs2266959). A further seven genes (SNPs) showed suggestive association (defined as an OR in a consistent direction and a p < 0.2) - ANKRD55 (rs71624119), CCR1-CCR3 (rs79893749), FAS (rs7069750), IL2-IL21 (rs1479924), IL2RB (rs2284033), RUNX3 (rs4648881) and TYK2 (rs34536442).
Forest Plot comparing CLARITY allelic ORs with published Immunochip ORs for each SNP genotyped.
We re-analysed the data after restricting the samples to those of European descent to ensure associations were not being masked by differences in ethnicity between the prior study and our own. Only those individuals who identified all four grandparents as European were included. Individuals with a non-European grandparent or any missing information were excluded. This restriction criterion reduced our sample size to 229 cases (mean age 6.2 years, 70% female) and 418 controls (mean age 7.2 years, 40% female). The results of this analysis are detailed in Additional file 1: Table S2. Overall, the results of this analysis did not considerably differ from the entire sample, with a few exceptions. Of note, RUNX3 (rs4648881), which had a suggestive association in the total sample, showed a significant association in the European-only sample. Furthermore, ZFP36L1 (rs12434551) showed significant association with JIA in the European-only sample, suggesting it may be particularly sensitive to ethnicity. However, our reported SNP (T) differs to the previously reported SNP (A), and the association is in the opposite direction. As this SNP is an A/T transversion with frequencies of both alleles near 50% we cannot determine whether the direction of effect difference is due to allele reversal, and thus replicates the prior result, or the result of analysing different DNA strands between the two studies, in which case the risk locus is not replicated. Two genes that were significantly associated in the entire sample, PRR5L (rs4755450), and AFF3-LONRF2 (rs6740838), were no longer significant in the European-only analysis, and C5orf56-IRF1 (rs4705862) showed only suggestive significance. Given that the reduced sample size likely resulted in a reduction in statistical power, small movements in the ORs and p values are not unexpected, and this result is unlikely to indicate failure to replicate.
We have replicated the association of AFF3-LONRF2 (rs6740838), and showed suggestive association of ANKRD55 (rs71624119) and IL2-IL21 (rs1479924), which all had evidence of association with JIA prior to the Immunochip study [ 11 – 13 ], although the samples used in those prior studies significantly overlapped with those used in the Immunochip study (i.e. the replication efforts were not independent). A recent independent study by Reinards et al. [ 14 ] also examined evidence for association of JIA with SNPs at ANKRD55 and AFF3 that are highly correlated (r 2 ~ 0.9) with the Immunochip SNPs at these genes. The association with ANKRD55 was replicated, further supporting a role for this gene in JIA risk. The association with AFF3 was not replicated, although the direction of effect is consistent across studies.
Although our study has been able to confirm association of a number of Immunochip-identified genes with JIA risk, lack of replication in our study cannot be taken as evidence to refute association for a number of reasons. First, our sample size is much smaller than that used by the Immunochip study, and therefore has lower statistical power to detect association. This is somewhat offset by the more relaxed level of significance required for replication, compared to that required in the discovery study. Second, the Immunochip study was restricted to oligoarticular and polyarticular RF negative JIA. Here we have considered all JIA subtypes together. We chose not to perform subtype specific analyses, as the benefit of reduced heterogeneity would be offset by the reduction in statistical power associated with reduced sample size. The fact that the associations identified in this study were still detectable when all subtypes were considered together suggests common pathways to disease in all subtypes (potential ‘pan-JIA’ genes). Such genes are not unexpected, given the significant overlap amongst disease risk genes across far more clinically heterogeneous forms of autoimmune disease [ 15 ]. Despite the sample size and case heterogeneity limitations of our study, it is worth noting that a number of the associations that we have failed to replicate only carried a suggestive level of association in the original study, and therefore our lack of replication may well be indicating lack of true association.
This study has further confirmed association of JIA with seven genetic loci, six of which have now been replicated for the first time ( C5orf56-IRF1 (rs4705862), ERAP2-LNPEP (rs27290), PRR5L (rs4755450), RUNX1 (rs9979383), RUNX3 (rs4648881), and UBE2L3 (rs2266959)). For an additional six genes, we generated weaker indication of association, further supporting (but not yet confirming) involvement in JIA risk. Further analysis of these loci in other independent samples will assist in clarifying their role in disease susceptibility. Overall, our study makes an important contribution to the growing body of international data that is revealing the genetic risk landscape of this paediatric autoimmune disease.
Electronic supplementary material
Additional file 1: Table S1: Significant/suggestive SNPs from the Immunochip study not included in this replication analysis. Table S2: CLARITY SNP replication results in European---restricted cohort, case n=220 control n=418. (PDF 83 KB)
Acknowledgements
We thank the participants and their families for their generous donation of information and biospecimens to this study, along with the research staff who recruited them. We acknowledge funding from the National Health and Medical Research Council of Australia (NHMRC, #APP1026349), along with a number of other philanthropic organisations including Arthritis Australia and the Rebecca Cooper Foundation. The work was supported by the Victorian State Government Operational Infrastructure Program. JAE is supported by an Australian Research Council Future Fellowship. ALP and RS are supported by National Health and Medical Research Council (Australia) Senior Research Fellowships.
Footnotes
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
JE and RCC conceived, designed and led the current analysis. JE and JM led the CLARITY study from which the participants were drawn. ALP led control collection, and advised and assisted with study design and collection of epidemiological data. RCC performed the SNP genotyping. RC managed biospecimens. AP managed the study data. RA and JA assisted with case study design, and JM, RA, JA and SP assisted with case recruitment through their paediatric rheumatology clinics. RS contributed to study design and supervision. RCC and JE wrote the manuscript, and all authors participated in drafting the manuscript to the final version. All authors read and approved the final manuscript.
Contributor Information
Rachel C Chiaroni-Clarke, Email: rachel.chiaroniclarke@mcri.edu.au.
Jane E Munro, Email: jane.munro@rch.org.au.
Raul A Chavez, Email: raul.chavez@mcri.edu.au.
Angela Pezic, Email: angela.pezic@mcri.edu.au.
Roger C Allen, Email: roger.allen@rch.org.au.
Jonathan D Akikusa, Email: jonathan.akikusa@rch.org.au.
Susan E Piper, Email: sue.piper@monash.edu.
Richard Saffery, Email: richard.saffery@mcri.edu.au.
Anne-Louise Ponsonby, Email: anne-louise.ponsonby@mcri.edu.au.
Justine A Ellis, Email: justine.ellis@mcri.edu.au.
References
Associated Data
Supplementary Materials
Additional file 1: Table S1: Significant/suggestive SNPs from the Immunochip study not included in this replication analysis. Table S2: CLARITY SNP replication results in European---restricted cohort, case n=220 control n=418. (PDF 83 KB)
Juvenile Idiopathic Arthritis (JIA) is the most common rheumatic disease in children [ 1 ], yet there are relatively few confirmed disease associated loci, and we are therefore limited in our understanding of the overall genetic architecture [ 2 , 3 ]. Only two genome-wide association studies have been published for JIA [ 4 , 5 ], limited by sample size and/or genomic coverage. In these studies, a discovery sample was used to identify potential risk loci, and then an independent replication sample was used to confirm the findings. In both studies, a number of loci were identified as associated with JIA in the discovery sample that were unable to be replicated in the validation sample [ 4 , 5 ]. This highlights the importance of independent replication in determining true disease associated loci.
In 2013, Hinks et al. published the most successful genetic association study for JIA to date [ 6 ], using the Immunochip [ 7 ]; a genotyping array specifically designed to densely capture variation at immune-related genetic loci. The study further validated the well-established associations of HLA, PTPN22 and PTPN2 with JIA, in addition to confirming the involvement of other loci such as STAT4 with JIA, that were previously associated with the disease, but not at a genome-wide level of significance (p < 5 × 10 -8 )[ 6 ]. More notably, the same study identified a suite of novel JIA risk loci reaching either genome-wide significance, or a genome-wide ‘suggestive’ level of significance (p < 1 × 10 -6 ) [ 6 ]. However, in order to maximize statistical power for discovery, all available JIA cases formed the discovery sample, and no replication sample was available to confirm these novel associations. Therefore, we aimed to confirm the involvement of these new JIA risk loci in an independent sample of JIA cases and healthy child controls drawn from the CLARITY JIA case–control study in Melbourne Australia [ 8 , 9 ].
We selected 22 single nucleotide polymorphisms (SNPs) from the JIA Immunochip association study [ 6 ], for potential replication. SNPs that are established susceptibility loci for JIA [ 4 ], or that have previously been associated in our sample [ 8 ] (either the SNP itself or a SNP in high linkage disequilibrium) were omitted. Two SNPs selected for inclusion subsequently failed assay design and were therefore also excluded. Reasons for exclusion are listed in Additional file 1: Table S1. Genotyping of the remaining 20 SNPs was attempted in a total of 408 JIA cases and 687 controls drawn from CLARITY, using the Sequenom MassARRAY system (assay design details available from the authors). Following exclusion of individuals and SNPs not meeting quality control thresholds (<90% call rate for individuals or SNPs, and departure from Hardy Weinberg equilibrium p < 0.001), genotype data was successfully generated for 404 JIA cases (mean age 6.4 years, 68% female) and 676 healthy child controls (mean age 7.1 years, 42% female) across 19 SNPs (listed in Table 1 ). One SNP, rs66718203 at PRM1-RM12 , failed genotyping quality control and was excluded from further analysis. We re-genotyped a random 10% selection of samples across all SNPs and achieved a genotype concordance rate of >99%. Allelic, genotypic, additive (Cochrane-Armitage test for trend), dominant and recessive association tests were performed using PLINK v1.07 [ 10 ]. We considered p < 0.05 for any test, along with an allelic odds ratio (OR) in a direction consistent with the previous report, as evidence of replication. As these SNPs had a priori evidence for association, we did not adjust the p values for multiple testing.
Table 1
CLARITY association replication results
Gene SNP Minor allele Case MAF/Control MAF Allelic Best Immunochip Replication
c
P OR (95% CI) P
a
P
b
OR (95% CI)
13q14
rs34132030 T 0.30/0.30 0.82 1.02 (0.85-1.24) 0.68 D 1.77 × 10 -7 A 1.18(1.11-1.26) N
AFF3-LONRF2
rs6740838 T 0.44/0.41 0.25 1.11 (0.93-1.32) 0.042 R 8.83 × 10 -7 D 1.25(1.14-1.37) Y
ANKRD55
rs71624119 A 0.20/0.23 0.10 0.83 (0.67-1.03) 0.081 R 4.40 × 10 -11 A 0.78(0.73-0.84) S
ATP8B2-IL6R
rs11265608 A 0.08/0.07 0.50 1.12 (0.81-1.55) 0.50 T 2.75 × 10 -8 D 1.33(1.20-1.47) N
C5orf56-IRF1
rs4705862 T 0.42/0.45 0.11 0.87 (0.73-1.04) 0.033 D 1.02 × 10 -8 A 0.84(0.79-0.89) Y
CCR1-CCR3
rs79893749 T 0.09/0.11 0.10 0.78 (0.58-1.05) 0.10 A 1.88 × 10 -7 A 0.78(0.72-0.86) S
ERAP2-LNPEP
rs27290 G 0.47/0.39 0.00021 1.40 (1.17-1.66) 0.00021 A 7.50 × 10 -9 D 1.32(1.20-1.45) Y
FAS
rs7069750 G 0.47/0.45 0.19 1.12 (0.94-1.34) 0.19 A 2.93 × 10 -8 A 1.18(1.11-1.25) S
IL2-IL21
rs1479924 G 0.27/0.28 0.54 0.94 (0.77-1.15) 0.16 R 6.24 × 10 -11 A 0.79(0.74-0.85) S
IL2RB
rs2284033 A 0.40/0.43 0.24 0.90 (0.75-1.08) 0.18 D 1.55 × 10 -8 A 0.84(0.79-0.89) S
IL6
rs7808122 T 0.41/0.40 0.76 1.03 (0.86-1.23) 0.59 R 5.80 × 10 -8 A 1.19(1.11-1.25) N
JAZF1
rs10280937 C 0.15/0.14 0.77 1.04 (0.81-1.33) 0.74 D 6.60 × 10 -7 A 1.25(1.15-1.37) N
LTBR
rs2364480 C 0.26/0.27 0.65 0.96 (0.78-1.16) 0.49 R 5.10 × 10 -8 A 1.20(1.12-1.28) N
PRR5L
rs4755450 A 0.34/0.38 0.043 0.83 (0.69-0.99) 0.042 D 3.35 × 10 -7 D 0.80(0.74-0.87) Y
RUNX1
rs9979383 C 0.32/0.37 0.013 0.79 (0.66-0.95) 0.013 A 1.06 × 10 -8 D 0.78(0.72-0.85) Y
RUNX3
rs4648881 A 0.48/0.50 0.37 0.92 (0.78-1.10) 0.19 D 4.66 × 10 -7 A 1.16(1.10-1.23) S*
TYK2
rs34536443 C 0.02/0.03 0.10 0.63 (0.36-1.10) 0.10 A 1.00 × 10 -10 A 0.56(0.47-0.67) S
UBE2L3
rs2266959 T 0.23/0.19 0.040 1.25 (1.01-1.55) 0.036 T 6.20 × 10 -9 D 1.24(1.15-1.33) Y
ZFP36L1
rs12434551 T 0.46/0.45 0.61 1.05 (0.88-1.25) 0.50 R 1.59 × 10 -8 D 0.77(0.71-0.85) N
a Model providing most significant P value: A = allelic, D = dominant, G = genotypic, R = recessive, T = trend (Cochrane-Armitage Trend Test, also referred to as additive).
b Immunochip model: A = additive, D = dominant.
c Evidence for replication Y = yes N = no S = suggestive * = opposite direction of effect due to the minor allele being opposite between the two studies.
Bold = reached genome-wide significance in Immunochip paper.
CLARITY association replication results
a Model providing most significant P value: A = allelic, D = dominant, G = genotypic, R = recessive, T = trend (Cochrane-Armitage Trend Test, also referred to as additive).
b Immunochip model: A = additive, D = dominant.
c Evidence for replication Y = yes N = no S = suggestive * = opposite direction of effect due to the minor allele being opposite between the two studies.
Bold = reached genome-wide significance in Immunochip paper.
Table 1 shows the ORs and p values for allelic association tests, as well as the ‘best test’ model and corresponding p value. These are shown alongside the reported ORs, p value, and model from the Immunochip study. Figure 1 shows a forest plot of the allelic odds ratios generated in the CLARITY sample compared to the Immunochip study ORs for all SNPs tested. We report replication of association for six genes (SNPs) in our sample - AFF3-LONRF2 (rs6740838), C5orf56-IRF1 (rs4705862), ERAP2-LNPEP (rs27290), PRR5L (rs4755450), RUNX1 (rs9979383) and UBE2L3 (rs2266959). A further seven genes (SNPs) showed suggestive association (defined as an OR in a consistent direction and a p < 0.2) - ANKRD55 (rs71624119), CCR1-CCR3 (rs79893749), FAS (rs7069750), IL2-IL21 (rs1479924), IL2RB (rs2284033), RUNX3 (rs4648881) and TYK2 (rs34536442).
Forest Plot comparing CLARITY allelic ORs with published Immunochip ORs for each SNP genotyped.
We re-analysed the data after restricting the samples to those of European descent to ensure associations were not being masked by differences in ethnicity between the prior study and our own. Only those individuals who identified all four grandparents as European were included. Individuals with a non-European grandparent or any missing information were excluded. This restriction criterion reduced our sample size to 229 cases (mean age 6.2 years, 70% female) and 418 controls (mean age 7.2 years, 40% female). The results of this analysis are detailed in Additional file 1: Table S2. Overall, the results of this analysis did not considerably differ from the entire sample, with a few exceptions. Of note, RUNX3 (rs4648881), which had a suggestive association in the total sample, showed a significant association in the European-only sample. Furthermore, ZFP36L1 (rs12434551) showed significant association with JIA in the European-only sample, suggesting it may be particularly sensitive to ethnicity. However, our reported SNP (T) differs to the previously reported SNP (A), and the association is in the opposite direction. As this SNP is an A/T transversion with frequencies of both alleles near 50% we cannot determine whether the direction of effect difference is due to allele reversal, and thus replicates the prior result, or the result of analysing different DNA strands between the two studies, in which case the risk locus is not replicated. Two genes that were significantly associated in the entire sample, PRR5L (rs4755450), and AFF3-LONRF2 (rs6740838), were no longer significant in the European-only analysis, and C5orf56-IRF1 (rs4705862) showed only suggestive significance. Given that the reduced sample size likely resulted in a reduction in statistical power, small movements in the ORs and p values are not unexpected, and this result is unlikely to indicate failure to replicate.
We have replicated the association of AFF3-LONRF2 (rs6740838), and showed suggestive association of ANKRD55 (rs71624119) and IL2-IL21 (rs1479924), which all had evidence of association with JIA prior to the Immunochip study [ 11 – 13 ], although the samples used in those prior studies significantly overlapped with those used in the Immunochip study (i.e. the replication efforts were not independent). A recent independent study by Reinards et al. [ 14 ] also examined evidence for association of JIA with SNPs at ANKRD55 and AFF3 that are highly correlated (r 2 ~ 0.9) with the Immunochip SNPs at these genes. The association with ANKRD55 was replicated, further supporting a role for this gene in JIA risk. The association with AFF3 was not replicated, although the direction of effect is consistent across studies.
Although our study has been able to confirm association of a number of Immunochip-identified genes with JIA risk, lack of replication in our study cannot be taken as evidence to refute association for a number of reasons. First, our sample size is much smaller than that used by the Immunochip study, and therefore has lower statistical power to detect association. This is somewhat offset by the more relaxed level of significance required for replication, compared to that required in the discovery study. Second, the Immunochip study was restricted to oligoarticular and polyarticular RF negative JIA. Here we have considered all JIA subtypes together. We chose not to perform subtype specific analyses, as the benefit of reduced heterogeneity would be offset by the reduction in statistical power associated with reduced sample size. The fact that the associations identified in this study were still detectable when all subtypes were considered together suggests common pathways to disease in all subtypes (potential ‘pan-JIA’ genes). Such genes are not unexpected, given the significant overlap amongst disease risk genes across far more clinically heterogeneous forms of autoimmune disease [ 15 ]. Despite the sample size and case heterogeneity limitations of our study, it is worth noting that a number of the associations that we have failed to replicate only carried a suggestive level of association in the original study, and therefore our lack of replication may well be indicating lack of true association.
This study has further confirmed association of JIA with seven genetic loci, six of which have now been replicated for the first time ( C5orf56-IRF1 (rs4705862), ERAP2-LNPEP (rs27290), PRR5L (rs4755450), RUNX1 (rs9979383), RUNX3 (rs4648881), and UBE2L3 (rs2266959)). For an additional six genes, we generated weaker indication of association, further supporting (but not yet confirming) involvement in JIA risk. Further analysis of these loci in other independent samples will assist in clarifying their role in disease susceptibility. Overall, our study makes an important contribution to the growing body of international data that is revealing the genetic risk landscape of this paediatric autoimmune disease.
Electronic supplementary material
Additional file 1: Table S1: Significant/suggestive SNPs from the Immunochip study not included in this replication analysis. Table S2: CLARITY SNP replication results in European---restricted cohort, case n=220 control n=418. (PDF 83 KB)
Acknowledgements
We thank the participants and their families for their generous donation of information and biospecimens to this study, along with the research staff who recruited them. We acknowledge funding from the National Health and Medical Research Council of Australia (NHMRC, #APP1026349), along with a number of other philanthropic organisations including Arthritis Australia and the Rebecca Cooper Foundation. The work was supported by the Victorian State Government Operational Infrastructure Program. JAE is supported by an Australian Research Council Future Fellowship. ALP and RS are supported by National Health and Medical Research Council (Australia) Senior Research Fellowships.
Footnotes
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
JE and RCC conceived, designed and led the current analysis. JE and JM led the CLARITY study from which the participants were drawn. ALP led control collection, and advised and assisted with study design and collection of epidemiological data. RCC performed the SNP genotyping. RC managed biospecimens. AP managed the study data. RA and JA assisted with case study design, and JM, RA, JA and SP assisted with case recruitment through their paediatric rheumatology clinics. RS contributed to study design and supervision. RCC and JE wrote the manuscript, and all authors participated in drafting the manuscript to the final version. All authors read and approved the final manuscript.
Contributor Information
Rachel C Chiaroni-Clarke, Email: rachel.chiaroniclarke@mcri.edu.au.
Jane E Munro, Email: jane.munro@rch.org.au.
Raul A Chavez, Email: raul.chavez@mcri.edu.au.
Angela Pezic, Email: angela.pezic@mcri.edu.au.
Roger C Allen, Email: roger.allen@rch.org.au.
Jonathan D Akikusa, Email: jonathan.akikusa@rch.org.au.
Susan E Piper, Email: sue.piper@monash.edu.
Richard Saffery, Email: richard.saffery@mcri.edu.au.
Anne-Louise Ponsonby, Email: anne-louise.ponsonby@mcri.edu.au.
Justine A Ellis, Email: justine.ellis@mcri.edu.au.
References
Associated Data
Supplementary Materials
Additional file 1: Table S1: Significant/suggestive SNPs from the Immunochip study not included in this replication analysis. Table S2: CLARITY SNP replication results in European---restricted cohort, case n=220 control n=418. (PDF 83 KB)