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Genome-wide association study reveals ethnicity-specific SNPs associated with ankylosing spondylitis in the Taiwanese population

Journal of Translational Medicine volume 20, Article number: 589 (2022) Cite this article

Abstract

Background

Ankylosing spondylitis (AS) is an autoimmune disease affecting mainly spine and sacroiliac joints and adjacent soft tissues. Genome-wide association studies (GWASs) are used to evaluate genetic associations and to predict genetic risk factors that determine the biological basis of disease susceptibility.

We aimed to explore the race-specific SNP susceptibility of AS in Taiwanese individuals and to investigate the association between HLA-B27 and AS susceptibility SNPs in Taiwan.

Methods

Genotyping data were collected from a medical center participating in the Taiwan Precision Medicine Initiative (TPMI) in the northern district of Taiwan. We designed a case–control study to identify AS susceptibility SNPs through GWAS. We searched the genome browser to find the corresponding susceptibility genes and used the GTEx database to confirm the regulation of gene expression. A polygenic risk score approach was also applied to evaluate the genetic variants in the prediction of developing AS.

Results

The results showed that the SNPs located on the sixth chromosome were related to higher susceptibility in the AS group. There was no overlap between our results and the susceptibility SNPs found in other races. The 12 tag SNPs located in the MHC region that were found through the linkage disequilibrium method had higher gene expression. Furthermore, Taiwanese people with HLA-B27 positivity had a higher proportion of minor alleles. This might be the reason that the AS prevalence is higher in Taiwan than in other countries. We developed AS polygenic risk score models with six different methods in which those with the top 10% polygenic risk had a fivefold increased risk of developing AS compared to the remaining group with low risk.

Conclusion

A total of 147 SNPs in the Taiwanese population were found to be statistically significantly associated with AS on the sixth pair of chromosomes and did not overlap with previously published sites in the GWAS Catalog. Whether those genes mapped by AS-associated SNPs are involved in AS and what the pathogenic mechanism of the mapped genes is remain to be further studied.

Introduction

Ankylosing spondylitis (AS), an autoinflammatory disorder, is an unusual but well-known cause of chronic back pain. Common signs of AS are joint pain and stiffness, typically occurring at joints of the spine, as well as in the pelvis, shoulders, or hips. AS may progress to symptoms such as deformed joints, limited lumbar movement, and reduced thoracic vertebral activity. Endochondral ossification slowly progressing to fusions of spinal segments is a major cause of the symptoms. The extreme pattern can lead to the bony fusion of vertebral joints and eventually become a disability. Currently, AS is incurable and thought to be caused mainly by uncharacterized genetic factors [1].

The prevalence of AS per ten thousand is approximately 18.6 in Europe, 18.0 in Asia, 10.2 in Latin America and 7.4 in Africa (South Africa). However, the prevalence of AS is extremely high in certain countries, such as Turkey (11.9–49.0), China (37.1 specifically in the Shenzhen area), Italy (37.0), Taiwan (33.7) and the USA (31.9) [2]. AS has a significant correlation with human leukocyte antigen B27 (HLA-B27). The prevalence of HLA-B27 in the AS population is higher than 90% [3]. A recent study suggested that upregulation of the tissue-nonspecific alkaline phosphatase (TNAP)-related pathway caused by misfolding of HLA-B27 may contribute to the abnormal osteogenesis of AS in both a cell model and animal model. Additionally, the therapeutic potential of agents inhibiting TNAP was shown, though some adverse effects have been reported [4, 5].

HLA-B27 is considered one of the most important genetic factors contributing to AS. However, only 1–2% of HLA-B27 carriers develop AS, so HLA-B27 is not always reliable as a diagnostic or prediction criterion [3]. Furthermore, populations with a higher AS prevalence do not have a significantly higher ratio of HLA-B27 carriers. The prevalence of HLA-B27 carriers is approximately 10% among Caucasians, 8% among Han Chinese and 6% among the general population in Taiwan [6]. Previous familial aggregation studies indicated that heritability affects a considerable proportion of individuals with AS susceptibility [7]. Furthermore, ethnicity-specific genetic factors might be associated with disease severity and the high prevalence in certain populations [8, 9].

Genome-wide association studies (GWASs) are used to investigate correlations between genetic variants and traits of interest, especially associations between SNPs and diseases [10]. In the past decade, several GWASs have investigated the risk SNPs associated with AS, and hundreds of risk SNPs have been identified [11,12,13,14,15,16,17,18,19,20]. One risk AS-associated SNP, rs17192932, is specific to the Turkish population, which has a low prevalence of HLA-B27; this suggests the existence of ethnicity-specific risk SNPs in certain populations [19]. To date, no GWAS has been performed to study AS in individuals of Taiwanese descent.

The Taiwan Precision Medicine Initiative (TPMI) has recruited volunteers to collect Taiwanese genetic data and develop precision-based medicine since 2018. We used data from Tri-Service General Hospital (TSGH), which has joined the TPMI, to perform a GWAS to investigate risk SNPs associated with AS in the Taiwanese population.

We aimed to explore the race-specific AS susceptibility SNPs in Taiwanese individuals and to investigate the association between HLA-B27 and the AS susceptibility SNPs in Taiwan.

Methods

Ethics

The protocol of this study was reviewed and approved by the Institutional Review Board of the Tri-Service General Hospital (NO.: B202005140).

Study participants and genotyping

All the participants employed in this study were recruited from TSGH to join the TPMI project. TPMI is held by Academia Sinica with a partnership of 15 top medical centers in Taiwan and aims to establish a database consisting of comprehensive clinical data and genetic profiles of one million participants. Participants were recruited from medical centers and genotyped by Academia Sinica. Briefly, approximately 5 mL of peripheral blood per participant was collected into EDTA vacutainers; genomic DNA was extracted from mononuclear cells and genotyped by TPMI SNP array following TPMI’s regular protocol. The TPMI SNP array is modified from Axiom Genome-Wide TWB (Taiwan Biobank) 2.0 Array Plate, can test approximately 130 thousand known risk variants, 580 thousand mapping SNPs and 20 thousand copy number variant markers based on Taiwanese reference genome data and Taiwan Biobank whole genome sequencing data. This TPMI project only recruited Taiwanese people of which the Southern Han population accounted for 95% and the Indigenous populations accounted for 2.3% [21].

Participants were allotted into two subcohorts based mainly on two batches of genotyping work. Assignment to the case or control group was based on the SOAP (The Subjective, Objective, Assessment and Plan) notes by clinicians. Participants diagnosed with AS were assigned to the case group, and the rest of the participants excluding those recruited from the Rheumatology, Immunology and Allergy Division were assigned to the control group to avoid sample contamination by those who were potentially but not yet diagnosed with AS. A total of 1442 participants, including 206 AS cases and 1236 normal participants, were enrolled in this study. There were 120 AS cases and 720 controls in the first batch and 86 AS cases and 516 controls in the second batch.

Association analysis

The quality control and association analysis was conducted by PLINK 1.9 [22]. Quality control was carried out following the pipeline of automated quality control [23]. Individuals with a missing rate of genotype data above 0.05, indicating poor genotype quality, and were excluded. The study population contained individuals with high relatedness, which may result in bias. Individuals were excluded if they had a heterozygosity rate higher than three standard deviations of the means or an identity-by-descent (IBD) higher than 0.1875, which is the median value between second- and third-degree relatives. Then, the sex and age factor were adjusted by a regression analysis. SNPs with call rates lower than 0.95, Hardy–Weinberg equilibrium P values lower than 1 × 10–4, or minor allele frequency (MAF) lower than 0.05 were excluded. SNPs with P values in association analysis lower than 5 × 10–8 were considered significant. The Manhattan plot and Quantile–Quantile plot were generated by the R package qqman. The susceptibility of SNP-AS was adjusted to sex and age variables by logistic regression conducted with PLINK 1.9 and is presented as an odds ratio with a 95% confidence interval. The command code can be found in GitHub project (https://github.com/LinWZ-tw/PLINK_AS_GWAS.git).

Functional genomics study of AS-associated SNPs

To further investigate the function or the pathogenic mechanism of risk SNPs, their locations and mapped genes were sorted from SNPnexus (https://www.snp-nexus.org/v4/).

The linkage disequilibrium (LD) of risk SNPs was analyzed by Haploview 4.2, and the LD pattern was generated by a web-based tool, LDmatrix (https://ldlink.nci.nih.gov/?tab=ldmatrix). The most significantly associated SNPs (with the lowest p value) were selected from each haplotype block as tag SNPs. The expression of genes mapped by tag SNPs was obtained from the Genotype-Tissue Expression (GTEx) portal, which was based on samples from donors in the United States [24]. The expression levels in skeletal muscle, fibroblasts, and whole blood were measured.

Polygenic risk score prediction

We computed a set of polygenic risk scores (PRSs) derived from the genotype data of combined cohorts following the guidelines reported previously [25]. In brief, we executed six mainstream PRS methods, including Clumping and Thresholding [26], Lassosum [27], LDPred2 [28], GenEpi [29], PRS-cs [30], and PRSice [31], following their protocols and default settings. To perform a fair evaluation in the PRS prediction, we performed the train-test split by first randomly selecting ten percent of the data as an independent testing dataset for the prediction performance evaluation. Next, we applied quality control to the training dataset from ninety percent of the data. We then performed GWAS analysis by PLINK and built the PRS models using different methods on the training dataset. Of note, all methods except GenEpi used the same GWAS summary statistics as the starting point but selected different SNPs for inclusion in the prediction models according to their algorithms by tuning parameters. Because GenEpi applies a machine learning-based approach to include pairwise epistasis of genes, GenEpi makes choices differently for which SNPs to include in comparison with the other five methods. Finally, the area under the receiver operator characteristic (ROC) curve and the odds ratio of each different PRS decile relative to the rest of the data was calculated for the testing dataset to compare the performances of the six PRS models.

To examine whether model predictive ability is linked to elevated disease risk among individuals with high PRSs, we constructed a strata plot according to the PRS decile used in several previous studies [32,33,34]. In essence, the testing cohort was divided into 10 strata of increasing PRS estimated by each method. Then, we compared the prevalence percentage of each stratum to the whole testing cohort to obtain odds ratios of the risk for developing AS.

Statistical analysis

SPSS 22.0 (IBM Crop.) and Excel 365 were used for all the data preparation, processing and analysis. Categorical variables are presented as counting numbers or percentages. Continuous variables are presented as the mean value with the standard error. Proportions with a two-tailed P value of less than 0.05 were considered statistically significant. The differences in variables by type were estimated by the chi-square test or t test. The proportions of SNPs interacting with HLA-B27 were estimated by the chi-square test and confirmed by logistic regression.

Results

Sample selection and characterization

After quality control, 1402 participants were enrolled, including 118 AS patients and 698 non-AS participants in the first batch and 81 AS and 505 normal participants in the second batch. Twenty-four participants were excluded in the first batch (3 had a high missing rate, 8 had a high heterozygosity rate, and 13 had a high IBD), and 16 participants were excluded in the second batch (7 had a high heterozygosity rate, and 9 had a high IBD) (Fig. 1). There were 88 (74.6%) male and 30 (25.4%) female AS patients in the first batch and 61 (75.3%) male and 20 (24.7%) female AS patients in the second batch. The mean ages were 39.63 (± 11.66) and 39.32 (± 13.95), respectively. (Table 1).

Fig. 1
figure 1

Flowchart of the study

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Table 1 Characterization of participants
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AS-associated SNPs in the taiwanese population

There were 147 AS-associated SNPs raised from two batches of association studies and the combined batch (Additional file 1: Table S1). The results of the first, second and third batches all showed that the significant AS-associated SNPs were clustered on chromosome 6 (Fig. 2A–C). The lambda values ranged from 1.07 to 1.09, which may indicate a low risk of or insufficiently corrected population stratification. (Fig. 2A–C).

Fig. 2
figure 2figure 2figure 2

AS-associated SNPs in the Taiwanese population. A The GWAS results presented as a Manhattan plot and QQ plot for the first cohort, (B) the second cohort and (C) the combination cohort. D The associated SNPs were picked up from a Venn diagram of the three association studies. E The locations of 147 SNPs raised in three association studies and their mapped genes. F The ratios of the SNP corresponding genes. Coding nonsyn: nonsynonymous coding region

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AS-associated genetic loci and corresponding genes

In total, 306 loci were found to be significant in the association analysis of the first batch (P < 10–8). A total of 161 loci were found to be significant in the second batch (P < 10–8). A total of 529 loci were found to be significant in the combined batch (P < 10–8). A Venn diagram was used to find the intersection. A total of 147 loci were found to be the most obviously related loci (Fig. 2D).

The 147 loci were annotated to their corresponding genes by the Ensemble and SNP nexus (Fig. 2E). Some SNPs may be annotated to more than one gene; therefore, the sum of SNP numbers by each corresponding gene exceeded 147.

The loci at introns accounted for 53%, the loci at intergenic regions accounted for 30%, the loci in noncoding regions accounted for 10%, the loci at the 5’UTR accounted for 4%, the loci at the nonsynonymous coding region accounted for 2%, and the loci at the 3’UTR account for 1%. (Fig. 2F).

Functional genomics of AS-associated SNPs

The LD analysis suggested that the 147 AS-associated SNPs could be assigned into 12 haplotype blocks. The Haploview program was used to exclude SNPs covering multiple bases in at least one allele. (Fig. 3A).

Fig. 3
figure 3

Functional genomics of AS-associated SNPs. A A total of 147 AS-associated SNPs were assigned to 12 haplotype blocks. B Haplotype Block 8. rs9266267 is the tag SNP in haplotype block 8

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The locus with the smallest p value in every haplotype block was used as the tag SNP. These 12 tag SNPs are the most representative loci of the 12 haplotype blocks. (Table 2).

Table 2 The tag SNPs of each haplotype block
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Three tag SNPs located in intergenic regions were not further studied, and the other nine tag SNPs were mapped to GNL1, CCHCR1, HLA-B, AL671883.3, MICA, and HCP5, among others. (Table 2) Nine of the 12 tag SNPs in the haplotype blocks corresponded to genes: (Table 2) GNL1: rs142577772 (OR = 11.36, p value = 1.45E-34); CCHCR1: rs2073716 (OR = 3.94, p value = 3.99E-21); HLA-B: rs7766452 (OR = 34.17, p value = 1.72E-51); rs9368671 (OR = 3.40, p value = 9.82E-19); rs28862571 (OR = 10.14, p value = 2.35E-36); AL671883.3: rs6936035 (OR = 4.46, p value = 7.33E-25); MICA: rs2251396 (OR = 3.33, p value = 6.43E-19); HCP5: rs3094228 (OR = 4.36, p value = 1.19E-22); and rs9688839 (OR = 4.18, p value = 1.07E-21). The other 3 tag SNPs were located at the intergenic region, so there was no corresponding gene.

The tag SNPs were correlated with the expression of HLA-C, HLA-B, HLA-S, MICA, MICB, CCHCR1, HCG20, HCG27, MIR6891, NCR3, PSORS1C3, C4A, C4B, CYP21A1P, CYP21A2, DDAH2, GLOT1, LINC00243, LY6G5B, NOTCH4, PPP1R18, RNF5, XXbac BPG248L24.12 and XXbac BPG181B23.7 in skeletal muscle, fibroblasts or whole blood (Additional file 2: Table S2). In Additional file 2: Table S2, A1 is the minor allele and A2 is the major allele. Normalized effect size (NES) is the value of the alternative allele/reference allele. Positive values indicate that the alternative allele is correlated with increased expression level, and vice versa.

Association between HLA-B27 and genotype with minor allele

The results of the association between HLA-B27 and the genotype with the minor allele are presented in Table 3. rs7766452 had the highest odds ratio (OR), and the 95% confidence interval (CI) of OR was 56.69–1317.97. This means that AS patients with minor allele genotypes (AG, AA) are 273.33 times more likely to be HLA-B27 positive than AS patients without minor allele genotypes (CC).

Table 3 Association between HLA-B27 and tag SNPs
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With rs9266267 being the exception, 11 tag SNPs were statistically significantly associated with HLA-B27 positivity (p value < 0.05). It might be inferred that “whether HLA-B27 is positive or not” is directly related to “whether the genotype has the minor allele genotype”.

Polygenic risk score differentiated patients with AS from controls

We developed several PRS models on the genotyping data of AS and control cohorts using six different methods (Fig. 4). According to the area under ROC curves on the independent testing dataset, GenEpi achieved the highest performance with an AUROC of 0.8109, and LDpred2 yielded the lowest performance with an AUROC of 0.7605 (Fig. 4A). Interestingly, among 227 SNPs used by GenEpi to estimate the PRS of developing AS, there were 110 SNP pairs and 7 single SNPs (Additional file 3: Table S3). The distribution of scaled GenEpi PRS remarkably differed between the AS case and control groups (Fig. 4B). A similar trend of AS-PRS distributions between the case and control groups was observed for the other five methods (Additional file 5: Figure S1). In addition, the strata plot indicates that the top decile showed a striking increase in the prevalence percentage in the last stratum (Additional file 5: Figure S2). In addition, the highest ORs of developing AS in the last stratum compared with the whole testing cohort were observed in all six methods (Fig. 4C). That is, individuals in the top PRS decile had over a fivefold increased risk of being diagnosed with AS compared to those in the lower PRS deciles (Additional file 4: Table S4).

Fig. 4
figure 4

Prediction of the polygenic risk score of AS. A Receiver operating characteristic (ROC) curve of the polygenic risk score (PRS) for the prediction of AS. The areas under the ROC curves (AUROCs) for the six PRS methods are reported in the figure legend box. B The distribution of PRSs of the AS cases and controls predicted by the GenEpi method (the blue dashed line represents the median PRS of the control group, and the yellow dashed line represents the median PRS of the case group). C Strata plot of the odds ratio for developing AS generated by different PRS methods. The X-axis shows the range of the decile quantiles, and the Y-axis shows the odds ratio when comparing the PRS of corresponding quantiles (e.g., [90, 100] corresponds to those individuals with PRSs between the 90th and 100th percentile of the cohort population) with the rest of the PRS quantiles of individuals. The bars show the 95% confidence intervals of the odds ratios. D The annotated genes with the consensus SNPs selected by at least three PRS methods in the trained AS-PRS models. The gray blocks indicate that the SNPs were used in the prediction model of the given methods

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Finally, we selected a set of SNPs used by at least three out of six methods as the important features in the trained AS-PRS models. Despite the diverse procedures in tuning PRS models for different algorithms, twenty-three human genes with consensus SNPs contributing to different PRS models are shown (Fig. 4D).

Discussion

This is the first GWAS of a Taiwanese AS population analyzed with the TPMI database. The association study followed a typical protocol for GWAS. We conducted two association studies based on two batches of genotyping data and one based on a dataset merging the two batches of genotyping data. Only the SNPs (n = 147) raised in three associated studies were considered AS-associated SNPs. The SNPs located on the sixth chromosome had higher susceptibility in the AS group. There was no overlap between our results and the susceptibility SNPs found in people of other races. These 147 AS-associated SNPs were assigned to 12 haplotype blocks. The SNP with the lowest p value among every haplotype block was considered the tag SNP. Nine tag SNPs corresponded to genes, and 11 tag SNPs had statistically significant associations between HLA-B27 and genotypes with minor alleles.

The precise pathogenesis of AS is still unknown. However, this autoimmune disease is related to multifactor interactions, such as genetic background, immune response, environmental factors, and microbial infection [3]. Since 1961, AS has been known to involve a non-sex-linked dominant hereditary mechanism [35]. Genetic effects have been identified as causative factors, accounting for more than 90% of the population variation [36]. Previous studies indicated that the major histocompatibility complex (MHC) on chromosome arm 6p and HLA-B27, one of the MHC-1 molecules, is strongly linked to and associated with AS [37, 38]. This is compatible with our results. Although approximately 95% of Caucasian patients with AS are HLA-B27 positive, only 8% of HLA-B27-positive individuals in the population develop the disease [39,40,41]. This means that HLA-B27 is essential for family inheritance but that there are still other genetic risk factors. HLA-B27 has a high degree of genetic polymorphism, and more than 100 known subtypes have been identified. The distinct subtypes are related to the prevalence of AS in the different regions of the world. The most significant subtypes associated with AS are HLA-B*27:05 (Caucasians), HLA-B*27:04 (Chinese), and HLA-B*27:02 (Mediterranean populations) [42, 43]. Laval’s whole-genome screening study indicated that genes localized to chromosomes 1p, 2q, 6p, 9q, 10q, 16q, and 19q were associated with AS [44].

GWASs have been used to map the patterns of inheritance for the SNP, the most common form of genomic variation [45, 46]. A GWAS in 2010 surveying AS in a large population of European descent revealed that multiple gene variants, including ARTS1, IL23R, ANTXR2 and IL1R2, confer AS risk [47]. In the past decade, the following GWASs have identified 113 SNPs affecting the risk of developing AS. Furthermore, an ongoing GWAS will likely identify more than 100 new risk loci [14, 17, 48]. However, GWASs of the Han Chinese AS population are few [12, 49], and no GWAS has been performed among the Han Taiwanese AS population. A previous study indicated that ethnic differences would lead to genetic heterogeneity in AS susceptibility. Some genes, including those in the 2p15, ERAP1, and NPEPPS–TBKBP1 regions, may still play a critical role in AS pathogenesis across diverse populations [50].

Our results revealed that the AS-associated SNPs were clustered around HLA-B27. While many of them were located in intergenetic regions (30%), the others could be mapped to a group of genes. Among them, 38 SNPs were mapped to HLA-B, and some were mapped to HCP5 (13 SNPs), AL671883.3 (9 SNPs), POU5F1 (7 SNPs), CCHCR1 (6 SNPs), LINC02571 (5 SNPs), MICB (5 SNPs), MUC22 (4 SNPs), TCF19 (4 SNPs), and MICA (4 SNPs), among others. (Fig. 3).

Data collected from GTEx Portal reveal that SNPs are associated with the expression level of their mapped genes. The tag SNP, rs142577772, of Haplotype block 1 is located in the 3 prime UTR of the GNL1 gene. The mutation position of rs142577772 is the CCCTC-binding factor binding site. This multifunctional transcription regulator might affect the expression of multiple epigenes [51]. The GNL1 gene and HLA-E gene present a high degree of linkage disequilibrium. There is a strong association between the HLA-E gene and AS haplotype [52]. It could be inferred that the GNL1 gene might be associated with AS. The tag SNP, rs2073716, of haplotype block 3 is located in the intron of the CCHCR1 gene. The CCHR1 locus may be protective against AS [53]. rs7766452 of haplotype block 5, rs9368671 of haplotype block 6 and rs28862571 of haplotype block 7 are located in introns of the HLB-B gene. The HLA-B gene is listed as an AS-related gene in the GWAS Catalog database. A GWAS of Turks and Iranians indicated that rs17192932, HLA-B*2705, HLA-B*2702 and HLA-B2707 are variants of HLA-B related to AS [19]. rs6936035 of haplotype block 9 is located in the intron of the AL671883.3 gene. The AL671883.3 gene was shown to be an AS-related gene in the GWAS Catalog database and previous studies [11, 14]. rs2251396 of haplotype block 10 is located in the intron of the MICA gene. The MICA gene is listed as an AS-related gene in the GWAS Catalog database and a previous study [13]. rs3094228 of haplotype block 11 and rs9688839 of haplotype block 12 are located in the introns of the HCP5 gene. Coit’s study indicated that the genetic variant present in the CpG methylation site in HCP5 determines its methylation status and is linked to HLA-B*27 status in AS patients [54].

HLA-B27 is a necessary factor for the development of AS. However, only 77.6% of subjects in our first batch and 73.7% of subjects in our second batch were HLA-B27 positive. The minor allele frequency in the case group was significantly higher than that in the South Han Chinese population (Additional file 2: Table S5). The genotype distribution of the three SNPs (rs2524069, rs2524067 and rs7766452) with the smallest p value in haplotype block 5 showed that the proportion of HLA-B27-positive people with minor alleles was higher than that of HLA-B27-negative people. Most people with HLA-B27 positivity carry only one minor allele. Most people who are HLA-B27 negative carry the major allele. This might infer that the proportion with minor alleles in the SNP is relatively high in the Taiwanese population. This would cause the prevalence of AS to be higher than that in other regions of the world, but the proportion of HLA-B27 is not higher.

In addition, we used six PRS methods to estimate the risks of developing AS in Taiwanese populations. Overall, six PRS models yielded good performance with an AUROC of approximately 0.76 (Fig. 4A), and the top 10% of PRSs showed at least a fivefold increase in developing AS compared to the remaining lower risk groups (Fig. 4C). It is noteworthy that we employed the train-test split method to evaluate the PRS models using the independent testing cohort to avoid the overfitting problem. Among the six AS-PRS models, the GenEpi model achieved the highest performance in terms of its AUROC value (Fig. 4A). In contrast to the other five methods, GenEpi applies a machine learning approach to identify the epistasis effect of joint genetic effects associated with AS. Indeed, GenEpi identified 110 significant SNP-SNP interactions across entire genomic loci harboring many different genes (Additional file 2: Table S2). The most significant interaction effect on AS was found between two SNPs, rs2844532 (near HLA-S gene) and rs2904599 (near MICA gene), with a p value of 1.628 × 10–125. Both the HLA-S and MICA genes have been implicated to have SNPs associated with AS in a previous study, whereas the joint effect of two SNPs within these two genes has not yet been suggested [13]. On the other hand, we identified several genomic loci, including 23 genes, as having consensus SNPs that contributed considerably to at least three PRS models (Fig. 3D). Among 23 genes, many have been suggested to be associated with AS and other related autoimmune diseases, such as psoriasis and vitiligo, in GWASs. For example, WASF5P and LINC02571 have both been reported to be significantly associated with vitiligo in the Chinese Han population by GWAS [55]. Additionally, HLA-B, HLA-C, and MICA are indeed AS-related genes, as discussed in the aforementioned paragraphs. Since six different PRS methods apply different algorithms to evaluate the importance of SNPs to estimate the risk of developing AS, the genes with consensus SNPs could be considered critical genetic information related to AS. Therefore, the genomic loci in Fig. 4D might potentially be used as amplicon-based genes for the prediction of AS.

Several limitations exist. First, the data are from the division of rheumatology of a single medical center of the TPMI. As a result, there might be some AS patients going to other hospitals due to non-AS medical problems and joining the TPMI. These AS patients might be classified as a control group, which would cause bias. Second, we obtained the data from medical records. These data lack detailed basic demographic variables, personal health behaviors and living environment exposure. In addition, this study lacks HLA-B27 genotyping data in a normal population because only patients with symptoms of AS had HLA-B27 detected. Therefore, we could not analyze the association between HLA-B27 and SNPs in the general population. While SNPs are likely to have an effect on their mapped genes, SNPs located in intergenic regions were not assigned to any genes to avoid error of prediction. Third, the AS susceptibility SNPs found in this study almost did not overlap with the related SNPs in the GWAS Catalog database. For some SNPs, there is no relevant information or gene expression data for the corresponding gene in the database of the GTEx portal. However, all data in the GTEx portal are from donors in the United States. This discrepancy might be due to ethnic differences. In addition, few GWASs have been performed among the Han Chinese population [12, 49]. Currently, there is no GWAS of AS populations in Taiwan. We have few previous data to compare.

Conclusion

We found 147 SNPs in the Taiwanese population that were statistically significantly associated with AS on the sixth pair of chromosomes, and the SNPs could be divided into 12 haplotype blocks (Table S1). There were 9 SNPs related to susceptibility to AS. These SNPs did not overlap with previously published sites on the GWAS Catalog. The prevalence of tag SNPs in the Taiwanese population is higher than that in other Asian populations with “1000 genomes” as reference (Table S5), which may explain why the AS prevalence is higher in the Taiwanese population than in other Asian countries. Further study will be needed in which researchers employ the risk SNPs revealed by this study as diagnosis biomarkers for populations outside of Taiwan. Whether those genes mapped by AS-associated SNPs are involved in AS and what the pathogenic mechanism of the mapped genes is remain to be further studied.

Data resources and software

Information on SNP genotyping can be found in TPMI (https://tpmi.ibms.sinica.edu.tw/www/en/). The association studies were conducted based mainly on PLINK 1.9 (https://zzz.bwh.harvard.edu/plink/) and R 3.6.1 (https://cran.r-project.org/). The LD of SNPs was analyzed by Haploview 4.2 (https://www.broadinstitute.org/haploview/haploview) and displayed by a web tool, LD Link (https://ldlink.nci.nih.gov/).

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

AS:

Ankylosing spondylitis

GWAS:

Genome-wide association study

SNP:

Single-nucleotide polymorphism

TPMI:

Taiwan Precision Medicine Initiative

MAF:

Minor allele frequency

IBD:

Identity by descent

LD:

Linkage disequilibrium

GTEx:

Genotype-tissue expression

PRS:

Polygenic risk scores

References

  1. Benjamin M, Toumi H, Suzuki D, Hayashi K, McGonagle D. Evidence for a distinctive pattern of bone formation in enthesophytes. Ann Rheum Dis. 2009;68:1003–10.

    Article  CAS  Google Scholar 

  2. Dean LE, Jones GT, MacDonald AG, Downham C, Sturrock RD, Macfarlane GJ. Global prevalence of ankylosing spondylitis. Rheumatology. 2014;53:650–7.

    Article  Google Scholar 

  3. Zhu W, He X, Cheng K, Zhang L, Chen D, Wang X, Qiu G, Cao X, Weng X. Ankylosing spondylitis: etiology, pathogenesis, and treatments. Bone Res. 2019;7:22.

    Article  Google Scholar 

  4. Liu CH, Raj S, Chen CH, Hung KH, Chou CT, Chen IH, Chien JT, Lin IY, Yang SY, Angata T, et al. HLA-B27-mediated activation of TNAP phosphatase promotes pathogenic syndesmophyte formation in ankylosing spondylitis. J Clin Invest. 2019;129:5357–73.

    Article  CAS  Google Scholar 

  5. Clarke J. HLA-B27-related pathway mediates syndesmophyte formation in AS. Nat Rev Rheumatol. 2020;16:3–3.

    Article  Google Scholar 

  6. Feltkamp TE, Mardjuadi A, Huang F, Chou CT. Spondyloarthropathies in eastern Asia. Curr Opin Rheumatol. 2001;13:285–90.

    Article  CAS  Google Scholar 

  7. Végvári A, Szabó Z, Szántó S, Glant TT, Mikecz K, Szekanecz Z. The genetic background of ankylosing spondylitis. Joint Bone Spine. 2009;76:623–8.

    Article  Google Scholar 

  8. Jamalyaria F, Ward MM, Assassi S, Learch TJ, Lee M, Gensler LS, Brown MA, Diekman L, Tahanan A, Rahbar MH, et al. Ethnicity and disease severity in ankylosing spondylitis a cross-sectional analysis of three ethnic groups. Clin Rheumatol. 2017;36:2359–64.

    Article  Google Scholar 

  9. Li Z, Wu X, Leo PJ, De Guzman E, Akkoc N, Breban M, Macfarlane GJ, Mahmoudi M, Marzo-Ortega H, Anderson LK, et al. Polygenic risk scores have high diagnostic capacity in ankylosing spondylitis. Ann Rheum Dis. 2021;80:1168–74.

    Article  CAS  Google Scholar 

  10. Manolio TA. Genomewide association studies and assessment of the risk of disease. N Engl J Med. 2010;363:166–76.

    Article  CAS  Google Scholar 

  11. Australo-Anglo-American Spondyloarthritis Consortium (TASC), Reveille JD, Sims AM, Danoy P, Evans DM, Leo P, Pointon JJ, Jin R, Zhou X, Bradbury LA, et al. Genome-wide association study of ankylosing spondylitis identifies non-MHC susceptibility loci. Nat Genet. 2010;42:123–7.

    Article  Google Scholar 

  12. Lin Z, Bei JX, Shen M, Li Q, Liao Z, Zhang Y, Lv Q, Wei Q, Low HQ, Guo YM, et al. A genome-wide association study in Han Chinese identifies new susceptibility loci for ankylosing spondylitis. Nat Genet. 2011;44:73–7.

    Article  Google Scholar 

  13. Evans DM, Spencer CC, Pointon JJ, Su Z, Harvey D, Kochan G, Oppermann U, Dilthey A, Pirinen M, Stone MA, et al. Interaction between ERAP1 and HLA-B27 in ankylosing spondylitis implicates peptide handling in the mechanism for HLA-B27 in disease susceptibility. Nat Genet. 2011;43:761–7.

    Article  CAS  Google Scholar 

  14. Cortes A, Hadler J, Pointon JP, Robinson PC, Karaderi T, Leo P, Cremin K, Pryce K, Harris J, Lee S, et al. Identification of multiple risk variants for ankylosing spondylitis through high-density genotyping of immune-related loci. Nat Genet. 2013;45:730–8.

    Article  CAS  Google Scholar 

  15. Robinson PC, Claushuis TA, Cortes A, Martin TM, Evans DM, Leo P, Mukhopadhyay P, Bradbury LA, Cremin K, Harris J, et al. Genetic dissection of acute anterior uveitis reveals similarities and differences in associations observed with ankylosing spondylitis. Arthr Rheumatol. 2015;67:140–51.

    Article  CAS  Google Scholar 

  16. Li YR, Li J, Zhao SD, Bradfield JP, Mentch FD, Maggadottir SM, Hou C, Abrams DJ, Chang D, Gao F, et al. Meta-analysis of shared genetic architecture across ten pediatric autoimmune diseases. Nat Med. 2015;21:1018–27.

    Article  CAS  Google Scholar 

  17. Ellinghaus D, Jostins L, Spain SL, Cortes A, Bethune J, Han B, Park YR, Raychaudhuri S, Pouget JG, Hübenthal M, et al. Analysis of five chronic inflammatory diseases identifies 27 new associations and highlights disease-specific patterns at shared loci. Nat Genet. 2016;48:510–8.

    Article  CAS  Google Scholar 

  18. Trochet H, Pirinen M, Band G, Jostins L, McVean G, Spencer CCA. Bayesian meta-analysis across genome-wide association studies of diverse phenotypes. Genet Epidemiol. 2019;43:532–47.

    Article  Google Scholar 

  19. Li Z, Akar S, Yarkan H, Lee SK, Cetin P, Can G, Kenar G, Capa F, Pamuk ON, Pehlivan Y, et al. Genome-wide association study in Turkish and Iranian populations identify rare familial Mediterranean fever gene (MEFV) polymorphisms associated with ankylosing spondylitis. PLoS Genet. 2019;15: e1008038.

    Article  CAS  Google Scholar 

  20. Huang XF, Li Z, De Guzman E, Robinson P, Gensler L, Ward MM, Rahbar MH, Lee M, Weisman MH, Macfarlane GJ, et al. Genomewide association study of acute anterior uveitis identifies new susceptibility loci. Invest Ophthalmol Vis Sci. 2020;61:3.

    Article  CAS  Google Scholar 

  21. Taiwan, The World Factbook [https://www.cia.gov/the-world-factbook/countries/taiwan/ - people-and-society]

  22. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75.

    Article  CAS  Google Scholar 

  23. Anderson CA, Pettersson FH, Clarke GM, Cardon LR, Morris AP, Zondervan KT. Data quality control in genetic case-control association studies. Nat Protoc. 2010;5:1564–73.

    Article  CAS  Google Scholar 

  24. Lonsdale J, Thomas J, Salvatore M, Phillips R, Lo E, Shad S, Hasz R, Walters G, Garcia F, Young N, et al. The Genotype-tissue expression (GTEx) project. Nat Genet. 2013;45:580–5.

    Article  CAS  Google Scholar 

  25. Choi SW, Mak TS, O’Reilly PF. Tutorial: a guide to performing polygenic risk score analyses. Nat Protoc. 2020;15:2759–72.

    Article  CAS  Google Scholar 

  26. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7.

    Article  Google Scholar 

  27. Mak TSH, Porsch RM, Choi SW, Zhou X, Sham PC. Polygenic scores via penalized regression on summary statistics. Genet Epidemiol. 2017;41:469–80.

    Article  Google Scholar 

  28. Privé F, Arbel J, Vilhjálmsson BJ. LDpred2: better, faster, stronger. Bioinformatics. 2020;36:5424–31.

    Article  Google Scholar 

  29. Chang YC, Wu JT, Hong MY, Tung YA, Hsieh PH, Yee SW, Giacomini KM, Oyang YJ, Chen CY. GenEpi: gene-based epistasis discovery using machine learning. BMC Bioinformatics. 2020;21:68.

    Article  Google Scholar 

  30. Ge T, Chen CY, Ni Y, Feng YA, Smoller JW. Polygenic prediction via bayesian regression and continuous shrinkage priors. Nat Commun. 2019;10:1776.

    Article  Google Scholar 

  31. Euesden J, Lewis CM, O’Reilly PF. PRSice: polygenic risk score software. Bioinformatics. 2015;31:1466–8.

    Article  CAS  Google Scholar 

  32. Khera AV, Chaffin M, Wade KH, Zahid S, Brancale J, Xia R, Distefano M, Senol-Cosar O, Haas ME, Bick A, et al. Polygenic prediction of weight and obesity trajectories from birth to adulthood. Cell. 2019;177:587-596.e589.

    Article  CAS  Google Scholar 

  33. Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, Natarajan P, Lander ES, Lubitz SA, Ellinor PT, Kathiresan S. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50:1219–24.

    Article  CAS  Google Scholar 

  34. Takahashi N, Nishimura T, Harada T, Okumura A, Choi D, Iwabuchi T, Kuwabara H, Takagai S, Nomura Y, Newcorn JH, et al. Polygenic risk score analysis revealed shared genetic background in attention deficit hyperactivity disorder and narcolepsy. Transl Psychiatry. 2020;10:284.

    Article  CAS  Google Scholar 

  35. de Blecourt J, Polman A, de Blécourt-Meindersma T. Hereditary factors in rheumatoid arthritis and ankylosing spondylitis. Ann Rheum Dis. 1961;20:215.

    Article  Google Scholar 

  36. Reveille JD. The genetic basis of ankylosing spondylitis. Curr Opin Rheumatol. 2006;18:332–41.

    Article  CAS  Google Scholar 

  37. Brewerton DA, Hart FD, Nicholls A, Caffrey M, James DC, Sturrock RD. Ankylosing spondylitis and HL-A 27. Lancet. 1973;1:904–7.

    Article  CAS  Google Scholar 

  38. Brown MA. Breakthroughs in genetic studies of ankylosing spondylitis. Rheumatology. 2008;47:132–7.

    Article  CAS  Google Scholar 

  39. Gran JT, Husby G, Hordvik M. Prevalence of ankylosing spondylitis in males and females in a young middle-aged population of Tromsø, northern Norway. Ann Rheum Dis. 1985;44:359–67.

    Article  CAS  Google Scholar 

  40. Braun J, Bollow M, Remlinger G, Eggens U, Rudwaleit M, Distler A, Sieper J. Prevalence of spondylarthropathies in HLA-B27 positive and negative blood donors. Arthr Rheum. 1998;41:58–67.

    Article  CAS  Google Scholar 

  41. van der Linden SM, Valkenburg HA, de Jongh BM, Cats A. The risk of developing ankylosing spondylitis in HLA-B27 positive individuals. a comparison of relatives of spondylitis patients with the general population. Arthr Rheum. 1984;27:241–9.

    Article  Google Scholar 

  42. Khan MA. Polymorphism of HLA-B27: 105 subtypes currently known. Curr Rheumatol Rep. 2013;15:362.

    Article  Google Scholar 

  43. Reveille JD. An update on the contribution of the MHC to AS susceptibility. Clin Rheumatol. 2014;33:749–57.

    Article  Google Scholar 

  44. Laval SH, Timms A, Edwards S, Bradbury L, Brophy S, Milicic A, Rubin L, Siminovitch KA, Weeks DE, Calin A, et al. Whole-genome screening in ankylosing spondylitis: evidence of non-MHC genetic-susceptibility loci. Am J Hum Genet. 2001;68:918–26.

    Article  CAS  Google Scholar 

  45. Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, Gibbs RA, Belmont JW, Boudreau A, Hardenbol P, Leal SM, et al. A second generation human haplotype map of over 31 million SNPs. Nature. 2007;449:851–61.

    Article  CAS  Google Scholar 

  46. The International HapMap Consortium. A haplotype map of the human genome. Nature 2005;437:1299–320. https://doi.org/10.1038/nature04226.

    Article  Google Scholar 

  47. Reveille J, Sims A, Danoy P, Evans D, Leo P, Pointon J, Jin R, Zhou X, Bradbury L, Appleton L, et al. Genome-wide association study of ankylosing spondylitis identifies non-MHC susceptibility loci. Nat Genet. 2010;42:123–7.

    Article  CAS  Google Scholar 

  48. Nancy Z, Yan L, Hui S, Paul B, Liye C. From the genetics of ankylosing spondylitis to new biology and drug target discovery. Front Immunol. 2021;12: 624632.

    Article  CAS  Google Scholar 

  49. Jung SH, Yim SH, Hu HJ, Lee KH, Lee JH, Sheen DH, Lim MK, Kim SY, Park SW, Kim SH, et al. Genome-wide copy number variation analysis identifies deletion variants associated with ankylosing spondylitis. Arthritis Rheumatol. 2014;66:2103–12.

    Article  CAS  Google Scholar 

  50. Zheng X, Li Q, Li X, Zhang Y, Wu X, Wei Q, Cao S, Yang M, Lin Z, Liao Z, et al. Analysis of 47 Non-MHC ankylosing spondylitis susceptibility loci regarding associated variants across whites and Han Chinese. J Rheumatol. 2020;47:674–81.

    Article  CAS  Google Scholar 

  51. Holwerda SJ, de Laat W. CTCF: the protein, the binding partners, the binding sites and their chromatin loops. Philos Trans R Soc Lond B Biol Sci. 2013;368:20120369.

    Article  Google Scholar 

  52. Paladini F, Belfiore F, Cocco E, Carcassi C, Cauli A, Vacca A, Fiorillo MT, Mathieu A, Cascino I, Sorrentino R. HLA-E gene polymorphism associates with ankylosing spondylitis in Sardinia. Arthritis Res Ther. 2009;11:R171.

    Article  Google Scholar 

  53. Mathioudaki A, Nordin J, Murén E, Karlsson Å, Olsson M, Pielberg G, Welander J, Kastbom A, Söderkvist P, Eriksson P: Replication and fine mapping of ankylosing spondylitis replicated loci in the Swedish population reveal different CCHCR1 protective haplotypes. DiVA 2019. (https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1341718&dswid=3429)

  54. Coit P, Kaushik P, Caplan L, Kerr GS, Walsh JA, Dubreuil M, Reimold A, Sawalha AH. Genome-wide DNA methylation analysis in ankylosing spondylitis identifies HLA-B*27 dependent and independent DNA methylation changes in whole blood. J Autoimmun. 2019;102:126–32.

    Article  CAS  Google Scholar 

  55. Quan C, Ren YQ, Xiang LH, Sun LD, Xu AE, Gao XH, Chen HD, Pu XM, Wu RN, Liang CZ, et al. Genome-wide association study for vitiligo identifies susceptibility loci at 6q27 and the MHC. Nat Genet. 2010;42:614–8.

    Article  CAS  Google Scholar 

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Acknowledgements

We thank all the participants and investigators from the Taiwan Precision Medicine Initiative. This study was funded by Academia Sinica 40-05-GMM and AS-GC-110-MD02. We thank the funding from Wan Fang Hospital (Program number: 110-phd-06). We also thank the Center for Precision Medicine and Genomics (CPMG), Tri-Service General Hospital, National Defense Medical Center, for helping us with gene information support.

Funding

This work was partially supported by grants (Wan Fang Hospital; Program number: 110-phd-06).

Author information

Authors and Affiliations

  1. Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, 114, Taiwan

    Ching-Lung Ko & Chi-Ming Chu

  2. Department of Anesthesiology, Wan Fang Hospital, Taipei Medical University, Taipei, 116, Taiwan

    Ching-Lung Ko

  3. School of Public Health, National Defense Medical Center, Taipei, 114, Taiwan

    Wei-Zhi Lin, Meng-Ting Lee, Yu-Tien Chang, Jun-Fu Lin & Chi-Ming Chu

  4. Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, 114, Taiwan

    Hung-Che Lin & Chih-Hung Wang

  5. Trauma and Critical Care Service, Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan

    Yi-Syuan Wu

  6. Graduate Institute of Aerospace and Undersea Medicine, National Defense Medical Center, Taipei, 114, Taiwan

    Ke-Ting Pan

  7. AI Labs, Taipei, 10351, Taiwan

    Yu-Chuan Chang, Ko-Han Lee, Yi-Lun Lee, Tsung-Ting Hsieh & Jia-Hsin Huang

  8. Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, 114, Taiwan

    Chih-Hung Wang, Sung-Sen Yang & Chi-Ming Chu

  9. Division of Nephrology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, 114, Taiwan

    Sung-Sen Yang

  10. Division of Rheumatology/Immunology and Allergy, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, 114, Taiwan

    Hsiang-Cheng Chen

  11. Big Data Research Center, College of Medicine, Fu-Jen Catholic University, New Taipei, 242, Taiwan

    Chi-Ming Chu

  12. Department of Public Health, Kaohsiung Medical University, Kaohsiung, 807, Taiwan

    Chi-Ming Chu

  13. Department of Public Health, China Medical University, Taichung, 406, Taiwan

    Chi-Ming Chu

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  1. Ching-Lung Ko
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Contributions

CLK, WZL, and MTL conducted the analysis and wrote the manuscript. YTC, HCL, YSW, JFL and KTP conducted the analyses. YCC, KHL, YLL and TTH conducted the computational analysis for the PRS analysis. JHH interpreted the results and wrote the manuscript portions concerning the PRS analysis. SSY and HCC provided original data. CHW and CMC organized and supervised this project. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Chih-Hung Wang, Sung-Sen Yang, Hsiang-Cheng Chen or Chi-Ming Chu.

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Ethics approval and consent to participate

The protocol of this study was reviewed and approved by the Institutional Review Board of the Tri-Service General Hospital (No.: B202005140).

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Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1: Table S1.

The 147 SNPs associated with AS

Additional file 2: Table S2.

The correlation of SNPs with gene expression in certain tissues. Table S5. The prevalence of tag SNPs in Asian populations.

Additional file 3: Table S3

. Information on selected SNPs in the six different PRS models.

Additional file 4: Table S4.

Odds ratio for developing AS according to PRS deciles.

Additional file 5: Figure S1.

Comparison of AS polygenic risk between AS cases and controls. Figure S2. Strata plot with ten strata of increasing PRS versus prevalence (%) of developing AS.

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Ko, CL., Lin, WZ., Lee, MT. et al. Genome-wide association study reveals ethnicity-specific SNPs associated with ankylosing spondylitis in the Taiwanese population. J Transl Med 20, 589 (2022). https://doi.org/10.1186/s12967-022-03701-3

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Keywords

  • Genome-wide association study
  • Ankylosing spondylitis
  • Single-nucleotide polymorphism
  • Taiwanese
  • SNPs
  • HLA-B27

Journal of Translational Medicine

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