Database, Vol. 2013, Article ID bat073, doi:10.1093/database/bat073.............................................................................................................................................................................................................................................................................................

Original article
RAvariome: a genetic risk variants databasefor rheumatoid arthritis based on assessmentof reproducibility between or within humanpopulations
Yoko Nagai1 and Tadashi Imanishi1,2,*
1
Department of Molecular Life Science, Division of Basic Medical Science and Molecular Medicine, Tokai University School of Medicine, 143
Shimokasuya, Isehara, Kanagawa 259-1193, Japan and 2Data Management and Integration Team, Molecular Profiling Research Center for Drug
Discovery, National Institute of Advanced Industrial Science and Technology, Koto-ku, Tokyo 135-0064, Japan*Correspondence author: Tel: +81 463 93 1121; Fax: +81 463 93 5418; Email:imanishi@tokai-u.jp
Submitted 14 August 2013; Revised 24 September 2013; Accepted 25 September 2013
Citation details: Nagai Y. and Imanishi T. RAvariome: a genetic risk variants database for rheumatoid arthritis based on assessment of reproducibility between or within human populations. Database, Vol. 2013, Article ID bat073, doi:10.1093/database/bat073.
.............................................................................................................................................................................................................................................................................................

Rheumatoid arthritis (RA) is a common autoimmune inflammatory disease of the joints and is caused by both genetic andenvironmental factors. In the past six years, genome-wide association studies (GWASs) have identified many risk variants
associated with RA. However, not all associations reported from GWASs are reproduced when tested in follow-up studies.
To establish a reliable set of RA risk variants, we systematically classified common variants identified in GWASs by the
degree of reproducibility among independent studies. We collected comprehensive genetic associations from 90 papers of
GWASs and meta-analysis. The genetic variants were assessed according to the statistical significance and reproducibility
between or within nine geographical populations. As a result, 82 and 19 single nucleotide polymorphisms (SNPs) wereconfirmed as intra- and inter-population-reproduced variants, respectively. Interestingly, majority of the intra-populationreproduced variants from European and East Asian populations were not common in two populations, but their nearby
genes appeared to be the components of common pathways. Furthermore, a tool to predict the individual’s genetic risk of
RA was developed to facilitate personalized medicine and preventive health care. For further clinical researches, the list ofreliable genetic variants of RA and the genetic risk prediction tool are provided by open access database RAvariome.
Database URL: http://hinv.jp/hinv/rav/.............................................................................................................................................................................................................................................................................................

Introduction
Rheumatoid arthritis (RA; MIM180300) is a common autoimmune disease characterized by the chronic inflammationof the bones and joints. Several epidemiological studies reported that RA prevalence varies among different populations (1). In North America and Northern Europe, theestimated prevalence of RA is 0.5–1.1%, but in Southern
Europe, a lower prevalence of 0.3–0.7% has been reported.
In East Asia, RA prevalence in the urban areas of Japan andTaiwan is 1.04% and 0.93%, respectively, but that inmainland China ranges from 0.2% to 0.37% (2, 3). Twin
studies on RA have led to a heritability estimate for RA of
65% in the Finnish study and 53% in the UK study, andgenetic factors account for an estimated 60% of the diseaserisk (4).
Large numbers of association studies have been conducted, and numerous single nucleotide polymorphisms(SNPs), genes and chromosomal regions have been reported
to be associated with RA. Several SNP-trait association databases provide information about RA-associated loci, including the NHGRI Catalog of Published Genome-Wide.............................................................................................................................................................................................................................................................................................

ß The Author(s) 2013. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/
licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly
cited.
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Association Studies (GWASs) (5), GWASdb (6), GWAS Central(7), HuGE Navigator (8) and PharmGKB (9). On the other
hand, some databases have been developed to provide
detailed genetic information of a specific disease, e.g.
AlzGene (http://www.alzgene.org/) for Alzheimer’s disease,
CADgene (http://www.bioguo.org/CADgene/) for coronary
artery disease and GOLD.db (https://gold.tugraz.at/) for
lipid-associated disorders. There are a few databases focusing
on autoimmune diseases; type 1 diabetes database T1Dbase(http://www.t1dbase.org) and Lymphoproliferative syndrome
database ALPSbase (http://research.nhgri.nih.gov/ALPS/).
As the first human genetic variation database for RA,
RAvariome aims to provide a reliable set of RA risk variants
that was systematically assessed according to its reproducibility. Candidate gene association studies and GWASs are
known to be vulnerable to a range of errors and biases,
especially those arising from differences in the experimental and study designs (10). These problems are reflected inequivocal or inconsistent results and may lead researchers
to design inappropriate follow-up studies or medical applications. Accordingly, we have collected association studies
comprehensively, classified the studies by ethnicity of subjects, re-evaluated the associations by unified significancelevel and assessed the associations by population-based reproducibility. All data are publicly available in a regularly
updated web database, RAvariome. Additionally, an online
tool for predicting the genetic risk of RA for an individual
was developed to support further analysis for preventive
intervention of genetic RA risk carrier.

Collection and Extraction of Data
We collected 153 English-language literatures from the
NHGRI catalog and HuGE Navigator and the automatic
paper recommendation system PubMedScan (http://
medals.jp/pubmedscan/) (Figure 1). Then, manual screeningof their abstracts excluded articles about other autoimmune diseases, pharmacogenomics and gene environment
studies. After filtering, 90 literatures about GWASs, finemapping studies and meta-analyses were kept for further
reading. Not only statistically significant but also statistically not significant association results, ethnicity of subjects,
the country where subjects were recruited, the total
number of cases and controls, the analysis platform andthe study design were extracted manually from full text,
tables or supplementary data. The association results ofhuman genetic variants included SNPs, HLA alleles, copy
number variations and variable number of tandem repeats
markers. Finally, 7730 association results were stored in a
database.
The results of statistical tests in original literatures werere-evaluated according to the following criteria. We determined two kinds of significance levels for GWAS thatassayed genome-wide SNPs and for follow-up study, suchas meta-analysis, that assayed only few SNPs of interest. Fora GWAS, if the corresponding P-value was <5.0  108, theresult of the statistical analysis was judged as significant
evidence of strong association, and P-value between
1.0  105 and 5.0  10–8 was judged as significant evidenceof moderate association. Associations with P > 1.0  105
were judged as not significant. For follow-up study, associations were judged as ‘strong’ for P < 0.01, ‘moderate’ forP-value between 0.01 and 0.05 and ‘not significant’ for
P > 0.05. Exceptionally, a meta-analysis of GWAS result
and combined analysis of GWAS and replication results
were judged by genome-wide significance threshold.
According to our significance level, 5970 associations wereclassified as statistically not significant, and 1760 associations were classified as either strong or moderateassociations.

Confirmation of Reproducibility of
RA Risk Variants
To confirm reproducibility of genetic associations based on ageographical information, all associations including nonsignificant results were grouped by the following nine populations; European (including European American, European
Australian, European New Zealander, West European, North
European, South European and East European), East Asian(including South-East Asian), West Asian, South Asian, SouthAmerican, Central American, North African, South Africanand African American. Because many follow-up studies ofprevious GWAS researches were conducted, the representative association for the variant in each geographical population was selected from the result of a study of the largest
number of case subjects. Out of 6740 representative associations, 1296 were statistically significant and remained forfurther reproducibility assessment. Notably, 34 variants
reached significant level in small-scale studies, but were
not confirmed by larger studies.
The genetic associations were classified into two classes
based on reproducibility between or within population. To
assess reproducibility of an association between different
populations, the variants that was reproduced by independent representative associations were identified. A genetic
variant that showed opposite direction of association between different populations was excluded. Accordingly, 40
representative associations of 19 variants located in 12 lociwere confirmed as ‘inter-population reproduced’ based on
independent studies of different populations such as
African American, Central American, East Asian,
European, South American, South Asian and West Asian(Table 1).
To assess reproducibility of an association within the particular population, we identified the statistically significant
representative association that was reproduced by an.............................................................................................................................................................................................................................................................................................

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Database, Vol. 2013, Article ID bat073, doi:10.1093/database/bat073

Original article.............................................................................................................................................................................................................................................................................................

Figure 1. The flowchart for the assessment of association results from articles. From comprehensive search by NHGRI GenomeWide Association catalog, HuGE Navigator and PubMedScan, 153 literatures were collected. In all, 7730 associations werecollected from 90 literatures by manual screening and reading. The associations were grouped into the nine geographicalpopulations based on sample information. A total of 6740 associations (6614 unique genetic variants) were chosen as representative associations of the variants in either population. In all, 5444 representative associations (5405 unique variants) did notreach significance level, and 1296 representative associations (1274 unique variants) were statistically significant and remainedfor further analysis. Forty representative associations of 19 variants were reproduced between different populations, and associations of 82 variants were reproduced in the particular population. The confirmed variants were used to predict genetic risk of
RA based on individual’s genotype. The data can be view and downloaded from the web database RAvariome.

.............................................................................................................................................................................................................................................................................................

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Page 4 of 9

rs805297-A
rs805297-A

rs3093023-A
rs3093023-A

rs231775-G
rs231775-G
rs231775

rs13192471-G
rs13192471-G
rs2157337-C
rs2157337-C
rs2516049-C
rs2516049-C
rs6457617-T
rs6457617-A
rs660895-G
rs660895-G
rs6910071-G
rs6910071

rs2872507-G
rs2872507-G
rs2872507-A

rs13119723-G
rs13119723-G
rs6822844
rs6822844

rs2104286
rs2104286-G

rs1800795-G
rs1800795-G

rs2230926
rs2230926-C
rs5029937-T
rs5029937

rs934734-G
rs934734-G

rs7574865-T
rs7574865

rs2793108-T
rs2793108-C

APOM

CCR6

CTLA4, ICOS

HLA-DRB1IKZF3aIL2, IL21

IL2RA

IL6

OLIG3, TNFAIP3

SPRED2

STAT4

ZEB1a
0.001
0.006

1.8e-006
2.99e-015

3.2e-007
5.3e-010

0.032
1.6e-006
3.9e-009
4.62e-010

1e-0087.4e-005

2.48e-05
0.00019

6.8e-007
0.008
5.89e-008
0.019

0.006
0.003
9.4e-007

1.9e-058
6.7e-016
2.6e-118
1e-2993.6e-031
1.64e-042
3.44e-076
1.6e-009
5.83e-053
2.56e-005
1e-299
0.04

0.01
0.002
0.009

2.1e-017
1.5e-011

6.65e-005
1.49e-016

P-value

Selected
Selected

GW
Selected (Meta)

GW
GW

Selected
GW
GW
Selected (Meta)

Selected
Selected

Selected (Meta)
Selected

GW
Selected
Selected (Meta)
Selected

Selected
Selected
GW

GW
Selected
GW
GW
GW
GW
GW
Selected
GW
Selected
GW
Selected

Selected
Selected (Meta)
Selected (Meta)

GW
GW

Selected
GW

Genotyping coverage

1.11
0.84

1.17
1.18

1.19
1.13

1.847
1.31
1.33
1.42

0.074
0.46

0.91
0.73

0.88
0.75
0.88
0.61

0.79
0.91
1.09

1.52
2.88
1.27

1.48

1.97
2.16
1.99
2.50
2.18

1.45
1.18
1.07

1.27
1.12

1.40

OR

2414/14 245
983/1007

7069/15 876
14 394/16 131

4074/16 891
12 307/28 975

148/1513
7069/15 876
4074/16 891
7731/9403

120/168425/247

10 112/10 450
983/1007

12 307/28 975
983/1007
12953/13370
354/368440/795
2414/14 245
12 307/28 975

2303/3380
983/1007
4074/16 891
5539/20 169
874/855
1147/1079
1861/2938
983/1007
1147/1079
983/1007
5539/20 169
983/1007

199/1992734/1441
6160/6684

4074/16 891
12 307/28 975

578/7111147/1079

Case/control numbers

East AsianSouth Asian

East Asian
European

East Asian
European

African American
East Asian
East Asian
European

East AsianWest Asian

EuropeanSouth Asian

EuropeanSouth Asian
EuropeanSouth American

African American
East Asian
European

East AsianSouth Asian
East Asian
European
East Asian
European
EuropeanSouth Asian
EuropeanSouth Asian
EuropeanSouth AsianCentral American
East Asian
European

East Asian
European

East Asian
EuropeanPopulation

Kurreeman FA, Am J Hum Genet, 2012 (21)Prasad P, PLoS One, 2012 (18)Kochi Y, Nat Genet, 2010 (17)Plant D, Ann Rheum Dis, 2010 (16)

Okada Y, Nat Genet, 2012 (13)Stahl EA, Nat Genet, 2010 (14)

Lee YH, Inflamm Res, 2012 (23)Kochi Y, Nat Gen*et, 2010 (17)Okada Y, Nat Genet, 2012 (13)Plant D, Ann Rheum Dis, 2010 (16)

Lee YH, Inflamm Res, 2012 (23)
Lee YH, Inflamm Res, 2012 (23)Plant D, Ann Rheum Dis, 2010 (16)Prasad P, PLoS One, 2012 (18)

Stahl EA, Nat Genet, 2010 (14)Prasad P, PLoS One, 2012 (18)Plant D, Ann Rheum Dis, 2010 (16)Maiti AK, Arthritis Rheum, 2010 (22)Kurreeman FA, Am J Hum Genet, 2012 (21)Kurreeman FA, Am J Hum Genet, 2012 (21)
Stahl EA, Nat Genet, 2010 (14)Kochi Y, Nat. Genet, 2010 (17)Prasad P, PLoS One, 2012 (18)
Okada Y, Nat Genet, 2012 (13)Stahl EA, Nat Genet, 2010 (14)Terao C, PLoS One, 2011 (19)Padyukov L, Ann Rheum Dis, 2011 (12)WTCCC, Nature, 2007 (20)
Prasad P, PLoS One, 2012 (18)
Padyukov L, Ann Rheum Dis, 2011 (12)Prasad P, PLoS One, 2012 (18)
Stahl EA, Nat Genet, 2010 (14)Prasad P, PLoS One, 2012 (18)

Li X, J Clin Immunol, 2012 (15)
Li X, J Clin Immunol, 2012 (15)Plant D, Ann Rheum Dis, 2010 (16)

Okada Y, Nat Genet, 2012 (13)Stahl EA, Nat. Genet, 2010 (14)Hu HJ, Exp Mol Med, 2011 (11)Padyukov L, Ann Rheum Dis, 2011 (12)ReferencesaConfirmed RA risk loci that were not mentioned in RA reviews (10, 24–27). Related genes, refSNP ID and allele, association P-value, study type to set significance level, OR, sample sizesof case/control, geographical population of samples and references are shown. Genotyping coverage indicated whether the study assayed genome-wide (GW) SNPs or selected SNPs.

SNP-allele

Related genes

Table 1. List of RA risk variants confirmed between different populations

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independent study of the same population. As a result,associations of 82 variants were confirmed as ‘intra-population reproduced’. Intra-population reproduced variants
were only reported in European and East Asian populations(53 SNPs and 29 SNPs, respectively).

Prediction of Genetic RA Risk
To facilitate personalized medicine and preventive healthcare, a tool to calculate genetic risk of RA for an individual
was developed based on confirmed RA risk variants (intraand inter-population-reproduced variants). To avoid overestimation, if confirmed RA risk variants are closely locatedto each other, i.e. in the same linkage disequilibrium block,
only the genetic variant with a smaller P-value was used asa risk marker of that locus.
The genetic risk score (GRS) and the relative genetic risk
(RGR) of RA for an individual were calculated by the combination of RA risk markers. As described in previous studies, an unweighted GRS, simply counting the number ofrisk alleles carried by an individual, was not applicable to
RA because HLA-DRB1 alleles had substantially higher odds
ratio (OR) than alleles at non-HLA loci (28–30). Accordingly,
we used a weighted GRS that increases additively by log-ORof the risk allele (31). GRS was calculated as follows:
GRS ¼

n
X

Xi log ORi ,i¼1

where n is the number of markers available and Xi represents the copy number of risk allele at a marker i. If an
individual carries 1 risk allele at ith marker, Xi is 1, and if
an individual carries 2 risk alleles, then Xi is 2.
To calculate RGR of the average population for eachmarker, an individual genotype-specific risk si is estimated as:
8< 1 if Xi ¼ 0
si ¼ ri if Xi ¼ 1 ,: 2
ri if Xi ¼ 2

where ri is a risk factor assumed to equal the allelic OR of
ith marker. RGR of individual marker is estimated as:
RGRi ¼si
ð1  pi Þ2 þ 2pi ð1  pi Þri þ p2i ri2:

Here pi is the risk allele frequency of ith marker in the control group. Thus, the overall RGR of an individual estimatedby multi-markers is calculated as:
RGRoverall ¼

nY

RGRi :

i¼1

RAvariome Web Interface andUsage
To provide our results and a genetic risk prediction tool for
researchers and clinicians, a web database RAvariome wasdeveloped. By comparing number of literatures and association data with other existing literature-based GWAS databases, RAvariome provides the most comprehensive datasetof associations of RA (Table 2). RAvariome consists of four
sections: overview of RA, the list of confirmed RA variants,
the collection of reported RA associations and genetic risk
prediction tool. In the overview section, the heritability,
environmental factors and protective factors of RA are
described.
The page entitled ‘Confirmed RA variants’ provides users
a list of genetic variants with the reproducibility class(Figure 2A). The list provides detailed study informationof the representative association of the specific variant
and the particular geographical population. In this section,
users can search the confirmed RA risk variants by the reproducibility class, the geographical population of the subjects, a gene name or refSNP ID.
The page entitled ‘Reported RA associations’ leads users
to a list of all statistically significant and statistically notsignificant associations described in literatures. Users cansearch association data by study design, geographicalpopulation of the subjects, nationality of the subjects,

Table 2. Comparing the number of literatures and associations of RAvariome with other literature-based databases
Database

Condition of literaturecollection

Number ofliteratures

Condition of data collection

Number of
associations

RAvariome

RA GWAS andfollow-up studies

90

All associations described in literature

7730

NHGRI GWAS catalog (5)GWAS literature

14a

Associations reached P-values <1.0  10596a

HuGE Navigator (8)GWAS literature
meta-analysisa

GWASdb (6)GWAS literature anddataset from GAD

84a

40
98a
Associations reached P-values <1.0  103

1748b

a

The number for HuGE Navigator and NHGRI GWAS catalog were obtained by searching the term ‘rheumatoid arthritis’ at 12 March
2013. bFor GWASdb, number of data was the result as of July 2012.

.............................................................................................................................................................................................................................................................................................

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Original article

Database, Vol. 2013, Article ID bat073, doi:10.1093/database/bat073.............................................................................................................................................................................................................................................................................................

Figure 2. Screenshots of web database RAvariome. (A) View of the confirmed RA variants of RAvariome. In the left sidebar, users
can search an association data by reproducibility class of genetic variants, population of subjects, genes and refSNP ID. The query
results will be shown in a tabular format. Users can get detailed study information by click on the detail icon on rightmost
column. The results can be easily downloaded as print format and comma-separated values (CSV) format. (B) The result of thegenetic RA risk predictor. Lifetime risk and effects of each risk markers based on selected genotypes are shown.

.............................................................................................................................................................................................................................................................................................

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CD40, HLA-DPB1, IRF5, CD58, BATF,
TRAF1, SH2B3
IL2RB, BATF, TRAF1, CCL21, CDK6,
PTPN22
HLA-DPB1, IRF5, IL2, IL2RA, IL2RB, IL6ST
CD28, IL2RA, CD226, IL6ST, PRKCQ,PTPN22, ANKRD55
CD40, PRKCQ, HLA-DRA, CD2, CCL21,
PTPN22
HLA-DPB1, CD28, HLA-DRA, CD2, PTPN22

IL2, REL, CDK6, DDX6, AFF3

IL2, IL2RA, PRKCQ
HLA-DPB1, SH2B3, HLA-DRA, PODXLREL, TRAF1
IL2, HLA-DRA

Genes upregulated by CD40 (GeneID = 958) signaling in
Ramos cells (EBV negative Burkitt lymphoma) (101).

Genes upregulated in TK6, WTK1 and NH32 cell lines(lymphoblast) in response to ionizing radiation (149).

Genes involved in cytokine signaling in immune system(270).

Genes whose promoters are bound by FOXP3(GeneID = 50943) based a ChIP-chip analysis (491).

Genes upregulated in the normal-like subtype of breast
cancer (476).

Genes upregulated in prostate cancer samples fromAfrican-American patients compared with those fromthe European-American patients.

Genes from common genomic gains observed in a metaanalysis of copy number alterations across a panel ofdifferent cancer cell lines and tumor samples (323).

Calcineurin-regulated NFAT-dependent transcription in
lymphocytes (47).

Genes downregulated in freshly isolated CD31
[GeneID = 5175] (stromal stem cells from adipose tissue)
versus the CD31+ (non-stem) counterparts (216).

Genes modulated in HeLa cells (cervical carcinoma) by TNF[GeneID = 7124] via NFKB pathway (28).

Cytokines and inflammatory response (29).

2.33E-04

2.17E-04

2.36E-05

6.72E-06

4.91E-06

3.38E-06

1.66E-06

8.89E-08

6.04E-08

1.74E-09

1.45E-12

3.89E-15

1.57E-02

1.55E-02

2.49E-03

8.24E-04

6.29E-04

4.83E-04

2.68E-04

2.67E-05

1.91E-05

8.73E-07

4.35E-09

2.34E-11

IL6, CSF2

CD83, NFKBIE, GCH1, IL6

PRKCH, FLI1, ANXA3, PDE2A

IRF4, CSF2

IRF4, FLI1, MN1

CD83, IRF8, FLI1

IL6, IRF8, FLI1, PDE2A

ETS1, ARID5B, B3GNT2, IRF4,PRKCH, FLI1, EXOC2

IL6, IRF8, IRF4, CSF2, PTPN2

CD83, NFKBIE, IRF4

CD83, NFKBIE, ARID5B, IRF4NFKBIE, IL6, IRF8,
IRF4, CSF2, PTPN2

1.11E-04

1.31E-09

5.27E-06

2.95E-04

6.79E-04

5.43E-04

1.15E-04

5.66E-09

3.05E-07

7.01E-05

2.54E-07

8.13E-06

P-value

1.47E-02

7.87E-06

1.90E-03

2.70E-02

4.39E-02

3.82E-02

1.47E-02

1.13E-05

2.29E-04

1.11E-02

2.18E-04

2.26E-03

FDR
q-valuea

The gene set enrichment analysis was performed by computing overlaps between the population unique genes and gene sets of positional (c1), curated (c2) and motif (c3) of MSigDB(32). The MSigDB gene sets listed in left column were significantly enriched in both populations at 5% false discovery rate (FDR) q-value.

aCD40, HLA-DPB1, IRF5, CD28, IL2, IL2RA,
CD226, IL2RB, TNFRSF14, IL6ST, C5,
PRKCQ, REL, KIF5A

Genes in overlap

FDR
q-valuea

Genes in overlap

P-value

East Asian unique 30 genes

European unique 39 genes

Genes involved in Immune System (933)

Description of MSigDB geneset (number of genes in gene set)

Table 3. Result of gene set enrichment analysis using population unique RA risk genes that was confirmed only in European and East Asian populations, respectively

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number of subjects and sub-phenotype of cases such ascases with 100% seropositive for rheumatoid factor and/or anti-CCP.
The page entitled ‘Genetic risk predictor’ provides a tool
for predicting the genetic risk of RA calculated by the ethnicity and genotypes of an individual (Figure 2B). By choosing a population from either European or East Asian, theconfirmed RA risk markers and their common three genotypes are displayed in the list. Users can select one genotype for each risk marker, and selected genotypes will be
used for estimation of RGR and GRS. RGR is the index thatassumed the risk of average population as baseline. GRS is
the index that assumed non-risk allele carrier as baseline
and used in basic researches. For example, if genotypes
AG for rs3761847, AA for rs2476601 and GA for
rs6920220 are selected at the European population, a predicted lifetime risk will be 2.6%, 2.6-fold higher than theprevalence of RA in general European population (1%).

Discussion
RAvariome was developed to provide a list of RA-associatedgenetic variants with the degree of reproducibility, basedon comprehensive re-evaluated results of the genetic
association studies. By comparing our results with reviews(10, 24–27) of RA, we found that two loci from inter-population-reproduced variants were not reported in reviews
and have been validated in recent meta-analyses (Table1). Thus, we conclude that our database provides thenewest and most reliable genetic risk variants based on
the comparative verification.
As the intra-population-reproduced variants wereobserved only in European and East Asian populations,
we used gene set enrichment analysis (32) to discovercommon functional mechanisms underlying different
gene sets confirmed specifically in European and East
Asian populations. From inter- and intra-reproduced-variants, 10 genes were confirmed in both European and East
Asian populations, although 39 and 30 genes were confirmed only in the European and East Asian populations,
respectively. Interestingly, the European and East Asianunique gene sets had the following gene sets in common:
genes involved in immune system, gene upregulated by
CD40 signaling in Ramos cells, genes upregulated in TK6,
WTK1 and NH32 cell lines in response to ionizing radiation,
genes modulated in HeLa cells by TNF via NFKB pathway,genes whose promoters are bounded by FOXP3 based on achromatin immunoprecipitation (ChIP)-chip analysis, genes
involved in cytokine signaling in immune system and genes
downregulated in freshly isolated CD31 versus the CD31+
counterparts (Table 3). This result suggested that even if
the same genes were not reproduced between European
and East Asian populations, several pathways werecommon in both populations.

The purpose of open access database RAvariome is to bea standard resource not only for RA researchers but also for
RA clinicians and the general public. RAvariome is therefore
designed as simple as possible to get confirmed RA genetic
risk variants for each geographical population. With ongoing progress in sequencing technology, the number ofgenetic studies of RA will continue to grow. RAvariome
will be periodically updated in concert with progress in
RA genetic research and will incorporate a new genetic
risk prediction method (33). An open access resource
would be valuable to raise the precision of the clinical genetic tests and to develop effective prevention programs of
RA based on genetic and population differences amongindividuals.

Acknowledgements
We thank C. Yamasaki, M. Sera, K. Murakami, Y. Hara and
T. Habara for their valuable suggestions and technical support for the database development. We also thank Dr
Naoyuki Kamatani and Dr Katsushi Tokunaga for theirthoughtful guidance.

Funding
AIST Innovation School to Y.N.; Grant-in-Aid for Scientific
Research on Innovative Areas (22133002). Funding for open
access charge: Grant-in-Aid for Scientific Research on
Innovative Areas (22133002).
Conflict of interest. None declared.

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