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Introduction

Psoriasis is a chronic, immune-mediated inflammatory skin disease (1). According to a study by the World Health Organization, it is a significant global health concern, affecting 125 million individuals worldwide (2). According to epidemiological studies, the global prevalence of psoriasis ranges from 1.0–11.4% (3). Specifically, the prevalence of psoriasis is 0.24% in Taiwan, as reported by the National Health Insurance Research Database for Taiwan (4); 0.340% in Japan (5); 0.453% in South Korea (6); and 0.470% in China (7). Psoriasis, a common dermatological condition, is characterized by persistent inflammation affecting several systems in addition to the skin (8). Psoriasis often causes significant psychological distress, which reduces the overall quality of life (9). Symptoms of psoriasis include distinct erythematous plaques on the skin, accompanied by silvery scales covering the affected areas, often accompanied by itching and pain (10). Clinical manifestations of psoriasis include subtypes such as plaque, guttate, nail, pustular, scalp and palmoplantar psoriasis (11,12). The etiology of psoriasis encompasses various elements, including genetic predispositions, environmental influences, immunologic responses (both innate and adaptive), lifestyle factors such as stress, bacterial or viral infections, and therapeutic interventions (3,4).

A characteristic feature of psoriasis is the hyper-proliferation of keratinocytes, fibroblasts, and endothelial cells, triggering immune system activation, inflammatory response and new blood vessel formation (neovascularization or angiogenesis) (10,13). Genetic predispositions and environmental influences are significant factors that trigger these effects (3,11). Genetic studies reveal that >10% of patients with psoriasis have a familial connection (14–16). Numerous genome-wide association studies (GWAS) conducted across various nations have identified >100 susceptibility loci associated with psoriasis, including human leukocyte antigen (HLA)-Cw6, tyrosine kinase 2 (TK2), interleukin-12B (IL-12B), interleukin-23 receptor (IL-23R) and late Cornified Envelope 3B/C (LCE3B/3C) (17–20). However, in Taiwan, no definitive genetic susceptibility loci or associated processes have been identified.

In clinical research, the polygenic risk score (PRS) is used as an analytical tool (21). Furthermore, the increase in sample size for GWAS has enhanced the power and effectiveness of the PRS. Therefore, a PRS based on GWAS is essential for personalized medicine (22). The PRS provides an accurate and efficient means of predicting the health status and susceptibility of an individual to disease (23). In addition to evaluating the genetic composition of an individual, PRS can provide valuable information regarding the efficacy of therapy and the likelihood of positive or negative outcomes, even before symptoms are evident (21,24). Furthermore, PRS provides an opportunity for early detection of abnormal test results before they occur (25,26). Through this approach, healthcare practitioners can prescribe preventive measures that are customized to the genetic profile of each patient (23,27,28).

In the present study, GWAS, PRS and phenome-wide association study (PheWAS) were conducted on gene variations in patients with psoriasis using the Taiwanese gene database of China Medical University Hospital (CMUH). A meta-analysis of single nucleotide polymorphism (SNP) sites associated with psoriasis in datasets from CMUH and BioBank Japan (BBJ) was also conducted (29). Notably, the genetic diversity among individuals from Taiwan is significantly different from that in other racial groups.

Materials and methods

Study design, sample sources and characteristics

The electronic medical records of the CMUH were analyzed for clinical data of patients with psoriasis between 2003 and 2020. All the included patients were validated by physicians with specific expertise in the field of psoriasis. The present study used the following International Classification of Diseases (ICD) diagnostic clinical modification (CM) codes: L40.0, L40.1, L40.4, L40.50, L40.8, L40.9, L41.3, L41.4, L41.5, L41.8 and L41.9; and the following ICD-9-CM codes: 696.1, 696.10, 696.2 and 696.8 (30). The number between ICD and CM indicates the version of ICD. Currently, the clinical practice of Taiwan includes two versions of ICD CM, versions 9 and 10. These two versions (ICD-9-CM and ICD-10-CM) have different codes, and therefore, their specifications are required. In Taiwan, clinicians mostly continue to record diagnoses using ICD-9 codes, which are then converted to ICD-10 codes through backend programs. This practice aligns with government regulations that mandated a complete transition to ICD-10 use for reporting purposes after 2015 (31). Regarding psoriasis diagnosis, there are no significant differences between ICD-9 and ICD-10. However, there is a primary difference in the coding for psoriasis between the two versions in terms of the level of detail and specificity. Under ICD-9, psoriasis was primarily coded as 696.1 for psoriatic arthropathy and 696.0 for other types of psoriasis without specifying the type of psoriasis. On the other hand, the ICD-10 offers a more detailed classification of psoriasis, allowing healthcare professionals to accurately specify the type and site of psoriasis. This increased specificity in ICD-10 enables improved tracking of the prevalence and treatment outcomes of various types of psoriasis, which in turn facilitates more accurate public health surveillance and research on psoriasis treatment and its effectiveness (32,33).

A schematic representation of the study design used to investigate psoriasis is shown in Fig. 1. The Precision Medicine Project was approved by the ethics committee of the institutional review board (approval nos. CMUH110-REC3-005 and CMUH111-REC1-176) and was approved by the Institutional Review Board of China Medical University Hospital (Taichung, Taiwan). Patient data, including genetic information (genetic variations detected by the TPMv1 SNP array), laboratory tests [C-reactive protein and erythrocyte sedimentation rate (ESR)], diagnoses, age and sex, were compiled for subsequent statistical analysis. The inclusion criteria were as follows: i) Patients with the ICD-9 and ICD-10 diagnostic codes for psoriasis; and ii) patients diagnosed with psoriasis at least three times at China Medical University Hospital. Examples of excluded autoimmune skin diseases include lupus erythematosus, dermatomyositis, systemic sclerosis (scleroderma), vitiligo and alopecia areata.

Figure 1. Flowchart of the experimental design on psoriasis. After the quality control of genetic markers and samples, dense genotype data were obtained through imputation. For GWAS and HLA sub-types analyses, the individuals were stratified into two groups, psoriasis and non-psoriasis. For the PRS analysis, the cohort was further divided into three groups: Base, target and validation groups (8:1:1). Finally, the bioinformatics network analysis and association between SNPs and human diseases were analyzed by IPA software. A meta-analysis of psoriasis was performed on the CMUH and BBJ. GWAS, genome-wide association study; HLA, human leukocyte antigen; PRS, polygenic risk score; SNP, single nucleotide polymorphism; IPA, Ingenuity Pathway Analysis; CMUH, China Medical University Hospital; BBJ, BioBank Japan; IQR, interquartile range; PCA, principal component analysis; INFO, imputation quality score; GP, genomic prediction; PheWAS, phenome-wide association.

The boxes on either side of the flow arrows in Fig. 1 represent exclusion criteria. In the analysis workflow, quality control (QC) was performed according the following steps using PLINK (https://www.cog-genomics.org/plink/; V.1.90). First, variations were checked for and those with a missing rate >10% (−geno 0.1) were eliminated. Next, the individuals were checked and those with a missing rate >10% (−mind 0.1) were removed. Next, adherence to the Hardy-Weinberg equilibrium by PLINK (https://www.cog-genomics.org/plink/; V.1.90) was verified for each variation, and variations that failed the hypothesis test with P>1×10−6 (−hwe 1×10−6) were removed (34,35). The occurrence rate for each variation was also calculated, which required a minor allele frequency of 1 in 10,000 (−maf 1×10−4). Additionally, the heterozygosity for each sample was examined and individuals with heterozygosity exceeding three times the interquartile range were excluded. Principal component analysis on each individual was performed and those who did not align with the East Asian populations from the 1,000 Genomes Project were excluded (36). After imputation, variations with an information score of <0.3 were removed to ensure accuracy. Variations with genotype probabilities <0.9 were also eliminated to guarantee reliability (37). GWAS and HLA subtype analyses were conducted on 2,248 patients with psoriasis and 67,440 controls (Fig. 1; right). The TPMv1 customized SNP array was obtained from Thermo Fisher Scientific, Inc., according to the manufacturer's instructions (38).

The association between the SNP array and psoriasis was investigated using PLINK (V.1.90). A Manhattan plot and quantile-quantile plot (QQ) (https://cran.r-project.org/web/packages/qqman/index.html; version 0.1.9) was generated using the R programming language (https://cran.r-project.org/; version: R 4.1.0) within the R Studio (https://posit.co/downloads/; version: R 1.4.1717) integrated development environment (38,39). Table SI presents the GWAS analysis results. The Gene Symbol and Entrez Gene Name in the last two columns are the information obtained after inputting into the Ingenuity Pathway Analysis (IPA) software and then merged into Table SI.

Study limitations

In Taiwan, under the regulations of the National Health Insurance, billing can be conducted solely with ICD codes, which do not categorize the severity of conditions (40). This applies to both the ICD-9 and ICD-10. In the present study, only ICD diagnosis codes were used for categorization, with diagnostic data spanning 19 years and being recorded by numerous physicians. Therefore, distinguishing severity levels among participants is challenging. Upon consultation with professional physicians, it was determined that only a minority would document severity levels in an unstructured manner for research purposes, further complicating the analysis owing to the unstructured nature of the data. Therefore, this limitation was added to the manuscript.

Biological networks analysis

GWAS was employed to analyze biological networks across the genome, with a significance threshold of P<5×10−8 (PLINK; V.1.90). This analysis led to the identification of a comprehensive set of 8,585 SNPs associated with psoriasis. A molecular network was constructed using core analysis in the IPA software (https://digitalinsights.qiagen.com/product-login/; version: 107193442; Qiagen Sciences, Inc.). The statistical significance of the available networks was assessed using Fisher's exact test with a significance level of P<0.05 (39,41,42).

Imputation and prediction of HLA genotypes

The HLA genotypes for each participant in the study were computed using HLA genotype imputation via attribute tagging (HIBAG-R) (https://bioconductor.org/packages/release/bioc/html/HIBAG.html; V1.5). HIBAG allowed for the determination of haplotypes and diplotypes for individuals, while maintaining P>0.90. Chi-square analysis was conducted to examine the HLA haplotypes and diplotypes to investigate the association between these genetic factors and the incidence of psoriasis. Bonferroni correction was applied to address multiple testing (39,43).

PRS analysis

To calculate the PRS, the CMUH cohort was divided into three distinct datasets including base, target and validation (8:1:1 ratio). PRS analysis was performed on 1,799 psoriasis patients and 53,952 controls in the base group, 225 psoriasis patients and 6,744 controls in the target group, and 224 psoriasis patients and 6,744 controls in validation group. A primary dataset was used to investigate the association between the variables under study and psoriasis using PLINK1.9. The PRS was calculated based on the target dataset and the PRSice2 (https://choishingwan.github.io/PRSice/; v2.3.3) tool while excluding variants with a missing rate of >1% and those deviating from the Hardy-Weinberg equilibrium with P<0.001. The present study utilized reference data from the 1,000 Genomes Phase 3 for the East Asian population (https://www.internationalgenome.org/data; v5b.20130502). The PRS was calculated by normalizing the z-scores. The PRS, clinical data, or a combination of both was used to construct logistic regression models. The models were subsequently adjusted for sex, age, HLA-A*02:07 and HLA-C*06:02 (22).

PheWAS

A genetic association between SNPs from PRS and diseases was constructed through logistic regression models, using the ‘PheWAS’ package (https://github.com/PheWAS/PheWAS; v0.12) in R programming language within the R Studio integrated development environment (https://cran.r-project.org/; R 4.1.0). PheWAS results were defined using the phecode schema with ICD diagnostic codes (10,750 unique ICD-10 codes and 3113 ICD-9 codes). Statistical significance of the available networks was assessed using Fisher's exact test with a significance level of P<5E-05 (44).

Meta-analysis of SNP loci on psoriasis

GWAS summary statistics for psoriasis were obtained from the BBJ (29). The summary statistics of psoriasis were downloaded from BBJ (https://pheweb.jp/downloads), and using the LiftOver method (https://hgdownload.soe.ucsc.edu/goldenPath/hs1/liftOver/; v1.26.0), the position of the BBJ genome construction gene, hg19, was transferred to GRCh38. In total 13,433,353 variations remained in the BBJ dataset. The meta-analysis was conducted using METAL software (https://genome.sph.umich.edu/wiki/METAL_Documentation; version, generic-metal-2011-03-25) (45).

Statistical analysis

Baseline continuous and categorical variables were examined using statistical tests such as the unpaired Student's t-test, χ2 test and Fisher's exact test. P<0.05 was considered to indicate a statistically significant difference. Statistical analyses were performed using SPSS (https://www.ibm.com/spss; version 22) and R (version R 4.1.0) software (46).

Results

Taiwanese GWAS of psoriasis

The present study presents the results of a GWAS involving 67,440 controls and 2,248 cases. The control group included 39,930 men (59.2%) and 27,510 women (40.8%), with a mean age of 45.75±16.798 years. The psoriasis cohort included 1,331 men (59.2%) and 917 women (40.8%), with a mean age of 45.75±16.801 years. The distribution of age and sex did not differ significantly between the groups (Table I). Habit-related clinical features such as smoking, alcohol consumption and betel consumption were not statistically significant. However, the comparison of ESR values between female patients with psoriasis and those in the control group revealed a statistically significant difference (P<5.34×10−11). The Manhattan (Fig. 2A) and QQ (Fig. 2B) plots are useful tools for visualizing GWAS results. The Manhattan plots (Fig. 2A) demonstrated the strongest associations between all the SNPs on a genomic scale. Notably, psoriasis was associated with 8,585 SNPs at a specific position: 14,064,987 (Table SI; P<5×10−8). As shown in Table II, SNP-induced amino acid mutations were significantly associated with psoriasis (P<5×10−8). Missense mutations were found in a variety of genes; furthermore, the rs34182778 variant was characterized by a DNA insertion within the corneodesmosin (CDSN) gene, whereas the rs200838925 variant was characterized by a DNA deletion within the mucin 22 (MUC22). The aforementioned findings suggested that these loci and gene mutations may share a common function, pathway or relevance to psoriasis. In the present study, the following 92 novel genomic markers for psoriasis were identified through GWAS and PRS (Table SI). Additionally, 61 previously reported genomic markers associated with psoriasis in Taiwanese populations were identified (Table SI): ATP binding cassette subfamily F member 1 (47), advanced glycosylation end-product specific receptor (AGER) (48), allograft inflammatory factor 1 (49), Annexin A6 (50), butyrophilin-like 2 (BTNL2) (51), complement C2 (52), chromosome 6 open reading frame 15 (53,54), complement factor B (52,55), fibroblast activation protein α (56), HLA complex group 26 (57), HLA complex group 27 (57), HLA complex group 9 (58), HLA complex P5 (59,60), HLA complex P5B (59,60), major histocompatibility complex, class I, A (HLA-A) (61), major histocompatibility complex, class I, B (HLA-B) (62,63), major histocompatibility complex, class I, C (HLA-C) (53,54), major histocompatibility complex, class II, DM β (HLA-DMB) (64), major histocompatibility complex, class II, DQ α1 (HLA-DQA1) (65), major histocompatibility complex, class II, DQ α2 (HLA-DQA2) (66), major histocompatibility complex, class II, DQ β1 (HLA-DQB1) (67), major histocompatibility complex, class II, DR β1 (HLA-DRB1) (67), major histocompatibility complex, class I, E (HLA-E) (68), major histocompatibility complex, class I, F (HLA-F) (69), major histocompatibility complex, class I, G (HLA-G) (70), heat shock protein family A (Hsp70) member 1A (HSPA1A/HSPA1B) (71), Hsp70 member 1-like (71), interleukin 12B (IL12B) (72,73), leukocyte specific transcript 1 (74), MHC class I polypeptide-related sequence A (MICA) (75), MICA antisense RNA 1 (75), MHC class I polypeptide-related sequence B (MICB) (76), MICB divergent transcript (76), myelin oligodendrocyte glycoprotein (77), mucin (MUC)21 (78), MUC22 (78), natural cytotoxicity triggering receptor 3 (NCR3) (79), negative elongation factor complex member E (80), notch receptor 4 (81–83), nurim (84), psoriasis susceptibility 1 candidate 1 (PSORS1C1) (85), PSORS1C2 (53,54,86), PSORS1C3 (57), ring finger protein 39 (87), transporter 1, ATP binding cassette subfamily B member (TAP1) (64), TAP2 (64), transcription factor 19 (57), tumor necrosis factor (TNF) (88,89), TNFAIP3 interacting protein 1 (TNIP1) (60), tenascin XB (TNXB) (88,89), tripartite motif containing 10 (TRIM10) (90), TRIM15 (91), TRIM26 (92), TRIM27 (92), TRIM40 (92), TSBP1 and BTNL2 antisense RNA1 (93), ubiquitin D (UBD) (94) and ubiquitin-like domain containing CTD phosphatase 1 (95).

Figure 2. Manhattan plots and quantile-quantile plots of genome-wide association studies for psoriasis. (A) Manhattan plot analysis for the association between genome-wide variations with psoriasis. SNPs that passed quality control are plotted on the x-axis, according to their chromosomal locations vs. the y-axis in the Manhattan plot analysis (−log10 P-value). The red line represents P=5×10−8, and the blue line represents P=1×10−5. (B) Quantile-Quantile plot analysis of the association of genome-wide variations with psoriasis (λ=1.011; based on median Chi-square). The red line in a QQ plot represents the reference line where expected values and observed values are the same. SNP, single nucleotide polymorphism; GC, group-specific component.

Table I. Selected characteristics of the study population.

Table I.

Selected characteristics of the study population.

Variables	Cases (n=2,248)	Controls (n=67,440)	P-value
Age, mean (SD)	45.75 (±16.801)	45.75 (±16.798)	>0.999
Sex, n (%)			>0.999
Male	1,131 (59.2)	39,930 (59.2)
Female	917 (40.8)	27,510 (40.8)
Lifestyle habits, n (%)
Smoking			0.853
Yes	37 (15.2)	5,727 (15.8)
No	207 (84.2)	30,424 (84.2)
Not available	2,004	31,289
Drinking			0.245
Yes	24 (9.8)	4,506 (12.5)
No	220 (90.2)	31,645 (87.5)
Not available	2,004	31,289
Betel nut			>0.999
Yes	14 (5.7)	2,090 (5.8)
No	230 (94.3)	34,061 (94.2)
Not available	2,004	31,289
Inflammation-related test values
CRP (mean, ±SD, n)	2.76, ±4.83, 74	3.83, ±6.16, 643	0.148
ESR (mean, ±SD, n)
Male	26.26, ±29.00, 821	24.85, ±31.24, 5,786	0.223
Female	31.92, ±28.70, 594	24.28, ±26.04, 3,927	5.34×10−11

[i] P<0.05 level. CRP, C-reactive protein; ESR, erythrocyte sedimentation rate.

Table II. SNP loci associated with amino acid variation site of GWAS in psoriasis (P<5×10−8).

Table II.

SNP loci associated with amino acid variation site of GWAS in psoriasis (P<5×10−8).

Chr.	ID	P-value	Gene symbol	DNA consequence	Amino acid position	Amino acid variation	SNP loci and psoriasis association	Gene and psoriasis association	(Refs.)
6	rs130076	4.05×10−34	CCHCR1	Missense	103, 109	R/W	Reported	Reported	(165)
6	rs9366785	8.28×10−21	PRRC2A	Missense	1,884	G/S	Novel	-	-
6	rs41257954	2.68×10−30	POU5F1	Missense	30	V/F	Novel	Reported	(166,167)
6	rs12722477	6.29×10−19	HLA-G	Missense	134	L/I	Novel	Reported	(113)
6	rs12211410	2.75×10−18	TNXB	Missense	1,255	R/P	Novel	Reported	(168)
6	rs1130363	4.95×10−18	HLA-G	Missense	333	R/S	Novel	Reported	(113)
6	rs118062293	1.12×10−17	LY6G6D	Missense	112	R/C	Novel	-	-
6	rs3132580	1.49×10−17	MUCL3	Missense	1,295	E/K	Novel	-	-
6	rs17207867	1.58×10−17	DXO	Missense	261	H/Q	Novel	-	-
6	rs3096696	4.19×10−17	PPT2	Missense	34	A/E	Novel	-	-
6	rs3096697	8.26×10−17	EGFL8	Missense	86	R/K	Novel	-	-
6	rs2227956	3.24×10−16	HSPA1L	Missense	493	T/M	Novel	Reported	(71)
6	rs28732176	3.48×10−16	FKBPL	Missense	90	A/T	Novel	-	-
6	rs2280801	5.22×10−16	PRRC2A	Missense	106	P/Q	Novel	-	-
6	rs1049622	7.49×10−16	DDR1	Missense	175	S/R	Novel	-	-
6	rs2233984	1.03×10−15	C6orf15	Missense	291	G/V	Novel	Reported	(53)
6	rs2074504	1.02×10−13	PRR3	Missense	159	H/Q	Novel	-	-
6	rs2074466	3.39×10−13	OR10C1	Missense	174	P/Q	Novel	-	-
6	rs61730668	3.84×10−13	MAS1L	Missense	72	A/V	Novel	-	-
6	rs9263726	5.47×10−13	PSORS1C1	Missense	37	R/H	Reported	Reported	(154)
6	rs3134900	5.89×10−13	MICB	Missense	121	I/M	Novel	Reported	(76)
6	rs2074469	7.35×10−13	OR10C1	Missense	60	F/L	Novel	-	-
6	rs9394078	8.47×10−13	ZBTB12	Missense	49	V/L	Novel	-	-
6	rs1576	1.61×10−12	CCHCR1	Missense	776	S/F	Reported	Reported	(169)
6	rs375368228	2.14×10−12	CDSN	Missense	509	L/P	Novel	Reported	(146)
6	rs2233953	4.50×10−12	PSORS1C2	Missense	84	P/S	Novel	Reported	(53)
6	rs187982887	5.36×10−12	DDR1	Missense	670	F/L	Novel	-	-
6	rs1052486	6.71×10−12	BAG6	Missense	619	S/A	Novel	-	-
6	rs72502536	8.62×10−12	MUC22	Missense	171	I/V	Novel	Reported	(78)
6	rs3734814	1.37×10−11	HLA-F	Missense	353	N/H	Novel	Reported	(69)
6	rs130066	1.49×10−11	CCHCR1	Missense	164	S/R	Reported	Reported	(165)
6	rs3734815	1.55×10−11	HLA-F	Missense	353	N/I	Novel	Reported	(114)
6	rs1046089	1.90×10−11	PRRC2A	Missense	1,740	R/H	Novel	-	-
6	rs34182778	2.02×10−11	CDSN	Insertions	149-150	GS/G	Novel	-	-
6	rs144128934	2.78×10−11	ZKSCAN4	Missense	88	S/T	Novel	-	-
6	rs3131787	2.85×10−11	SFTA2	Missense	37	N/S	Novel	-	-
6	rs9380254	5.96×10−11	MICA	Missense	16	R/H	Novel	Reported	(75)
6	rs2294746	8.20×10−11	OR2I1P	Missense	227	C/W	Novel	-	-
6	rs707926	2.52×10−10	VARS1	Missense	875	D/E	Novel	-	-
6	rs1059510	3.17×10−10	HLA-E	Missense	98	N/K	Novel	Reported	(68,115)
6	rs204883	3.21×10−10	TNXB	Missense	2,232	D/E	Novel	Reported	(168)
6	rs12722482	4.15×10−10	HLA-G	Missense	282	T/K	Novel	Reported	(113,170)
6	rs3130453	4.44×10−10	CCHCR1	Missense	78	W/C	Reported	Reported	(171)
6	rs143175221	8.81×10−10	HFE	Missense	295	V/A	Novel	-	-
6	rs11965542	1.75×10−9	ZSCAN26	Missense	83	E/K	Novel	-	-
6	rs149494377	3.08×10−9	H2AC15	Missense	48	A/G	Novel	-	-
6	rs61978565	3.16×10−9	OR11A1	Missense	80	M/T	Novel	-	-
6	rs76142796	3.17×10−9	OR12D1	Missense	111	M/T	Novel	-	-
6	rs79293918	4.25×10−9	OR2J3	Missense	240	V/A	Novel	-	-
6	rs9267799	4.64×10−9	TNXB	Missense	1,414	R/Q	Novel	Reported	(168)
6	rs61732185	5.64×10−9	OR2H1	Missense	63	D/N	Novel	-	-
6	rs117708355	5.78×10−9	MDC1	Missense	533	S/L	Novel	-	-
6	rs1150723	7.34×10−9	PGBD1	Missense	42	I/M	Novel	-	-
6	rs76463649	8.43×10−9	ZSCAN26	Missense	15	N/S	Novel	-	-
6	rs9267795	9.40×10−9	TNXB	Missense	3,214	G/V	Novel	Reported	(168)
6	rs200838925	9.71×10−9	MUC22	Deletion	983-1,005	ETTTASTEGS	Novel	Reported	(78)
						ETTTASTEGS
						ETT/ETTTAS
						TEGSETT
6	rs1042178	1.10×10−8	HLA-DPA1	Missense	81	Q/L	Novel	-	-
6	rs9394021	1.19×10−8	VARS2	Missense	917	R/Q	Novel	-	-
6	rs1126542	1.20×10−8	HLA-DPA1	Missense	114	T/S	Novel	-	-
6	rs61742983	1.36×10−8	OR5V1	Missense	233	G/R	Novel	-	-
6	rs3873352	1.58×10−8	HCG22	Missense	3	R/S	Novel	Reported	(172)
6	rs2308930	1.61×10−8	HLA-DPA1	Missense	158	L/P	Novel	-	-
6	rs1042308	1.81×10−8	HLA-DPA1	Missense	191	F/V	Novel	-	-
6	rs61736085	2.01×10−8	POM121L2	Missense	862	T/I	Novel	-	-
6	rs1140700	2.10×10−8	MICA	Missense	127	I/K	Novel	Reported	(75)
6	rs9257694	2.90×10−8	OR14J1	Missense	7	M/T	Novel	-	-
6	rs41266821	3.02×10−8	H4C7	Missense	3	V/A	Novel	-	-
6	rs2076486	3.36×10−8	UBD	Missense	95	S/A	Novel	-	-
6	rs2076484	3.36×10−8	UBD	Missense	51	L/S	Novel	-	-
6	rs2076487	3.48×10−8	UBD	Missense	99	A/G	Novel	-	-
6	rs16895067	3.85×10−8	OR2I1P	Missense	174	D/G	Novel	-	-
6	rs16895070	3.85×10−8	OR2I1P	Missense	188	C/R	Novel	-	-
6	rs7757931	3.85×10−8	UBD	Missense	162	C/F	Novel	-	-
6	rs1063635	4.10×10−8	MICA	Missense	165	R/Q	Novel	Reported	(75)
6	rs1126534	4.49×10−8	HLA-DPA1	Missense	42	A/V	Novel	-	-
6	rs1126533	4.49×10−8	HLA-DPA1	Missense	42	A/T	Novel	-	-
6	rs1009382	4.96×10−8	TNXB	Missense	2,518	G/V	Novel	Reported	(168)

[i] SNP, single nucleotide polymorphism.

HLA genotyping and allele frequency analysis

It is well established that certain HLA alleles and diplotypes are associated with an increased risk of psoriasis. High-resolution imputation was used to analyze the HLA genotypes and their corresponding allele frequencies associated with psoriasis. Table III shows the diplotypes identified in the sample and those that exhibited a significant association with psoriasis are listed in Table IV. Table SII presents raw data regarding HLA genotypes and allele frequencies that were significantly associated with psoriasis. The findings of the present study indicated that the HLA-A*02:07 (adjusted P=3.69×10−34) and HLA-C*06:02 (adjusted P=2.96×10−29) alleles exhibited the strongest association with psoriasis in the Taiwanese population.

Table III. Top 10 HLA diplotypes significantly associated with psoriasis (adjust P<1×10−5).

Table III.

Top 10 HLA diplotypes significantly associated with psoriasis (adjust P<1×10−5).

Rank	HLA genotype (HLA diplotypes)	Adjust P-value	L95	U95	OR	Control	Case	SE	β
1	HLA-C*01:02-*06:02	3.59×10−19	3.091	5.286	4.071	481.000	65.000	0.132	1.404
2	HLA-A*11:01-*33:03	1.13×10−15	0.289	0.507	0.387	4091.000	53.000	0.138	−0.949
3	HLA-A*02:07-*11:01	7.88×10−11	1.453	1.961	1.692	3389.000	190.000	0.075	0.526
4	HLA-A*02:07-*30:01	6.92×10−10	2.939	6.905	4.587	177.000	27.000	0.207	1.523
5	HLA-B*13:02-*46:01	8.92×10−10	2.646	5.813	3.985	234.000	31.000	0.191	1.382
6	HLA-C*06:02-*12:02	7.48×10−9	3.365	9.598	5.827	98.000	19.000	0.251	1.763
7	HLA-DQB1*02:01-*03:01	2.41×10−7	0.326	0.643	0.465	2384.000	37.000	0.166	−0.766
8	HLA-B*46:01-*46:01	9.27×10−7	1.506	2.482	1.947	1082.000	70.000	0.124	0.666
9	HLA-B*46:01-*57:01	1.60×10−6	4.143	21.944	10.017	27.000	9.000	0.385	2.304
10	HLA-A*24:02-*33:03	1.67×10−6	0.369	0.692	0.512	2517.000	43.000	0.154	−0.670

[i] HLA, human leukocyte antigen; OR, odds ratio; SE, standard error; L95, lower 95% confidence interval; U95, upper 95% confidence interval.

Table IV. Top 10 HLA allele frequencies significantly associated with psoriasis (adjust P<1×10−5).

Table IV.

Top 10 HLA allele frequencies significantly associated with psoriasis (adjust P<1×10−5).

Rank	Allele frequency (HLA haplotypes)	Adjust P-value	L95	U95	OR	Control	Case	SE	β
1	HLA-A*02:07	3.69×10−34	1.602	1.894	1.743	10510.000	605.000	0.042	0.556
2	HLA-C*06:02	2.96×10−29	2.205	2.964	2.563	2341.000	199.000	0.074	0.941
3	HLA-B*46:01	7.19×10−22	1.396	1.640	1.514	12905.000	646.000	0.041	0.415
4	HLA-A*33:03	5.02×10−21	0.503	0.650	0.573	12818.000	246.000	0.065	−0.557
5	HLA-C*03:02	5.11×10−17	0.545	0.697	0.617	13015.000	269.000	0.062	−0.482
6	HLA-B*13:02	5.42×10−15	1.923	2.844	2.348	1461.000	114.000	0.097	0.854
7	HLA-B*58:01	9.21×10−13	0.567	0.735	0.647	11230.000	243.000	0.065	−0.435
8	HLA-C*01:02	8.44×10−12	1.191	1.365	1.276	21439.000	905.000	0.034	0.244
9	HLA-DQA1*02:01	1.13×10−11	1.606	2.282	1.921	2178.000	139.000	0.088	0.653
10	HLA-DQB1*02:02	2.53×10−11	1.573	2.223	1.876	2294.000	143.000	0.086	0.629

[i] HLA, human leukocyte antigen; OR, odds ratio; SE, standard error; L95, lower 95% confidence interval; U95, upper 95% confidence interval.

Network analysis of SNPs associated with psoriasis on GWAS models

A comprehensive analysis was carried out using Bioinformatics IPA software to analyze 8,585 SNPs associated with psoriasis. These SNPs were assessed for significance in the context of the entire genome, using a threshold of P<5×10−8. The present study findings revealed that psoriasis is characterized by multiple key pathways, including immune responses (antigen presentation, PD-1/PD-L1 cancer immunotherapy, IL-10 signaling, interplay between dendritic cells and natural killer cells and Th1/Th2 activation) and inflammatory signaling (multiple sclerosis and neuroinflammatory signaling pathways), which are shown in Table V. Furthermore, gene numbers were cross-analyzed with pathways, resulting in the ranking of various biological processes: Cellular immune response, humoral immune response, cytokine signaling, cancer, disease-specific pathways, neurotransmitters and nervous system signaling, cellular growth, proliferation and development of neurotransmitters, pathogen-influenced signaling, cellular stress and injury, intracellular and second messenger signaling, apoptosis, organismal growth and development, nuclear receptor signaling, biosynthesis, and metabolic clusters (Fig. 3). Our GWAS and network analysis findings suggested that the diversity of specific genes, including HLA-A, HLA-C, HLA-DM, HLA-DP, HLA-DQ, psoriasis susceptibility 1 (PSORS1), HLA complex group on chromosome 6 and IL-12B on chromosome 5, play a crucial role in the development of psoriasis (Fig. 4A). Additionally, our results showed significant association with psoriasis between AGER, discoidin domain receptor tyrosine kinase 1 (DDR1), TNF, coiled-coil α-helical rod protein 1 (CCHCR1), tubulin β class I (TUBB) and TNXB genes (Fig. 4B). Furthermore, the regional association plot (linkage disequilibrium score r2>0.4) demonstrated a strong association between variations in the IL12B gene and psoriasis (Fig. 5A). Finally, the psoriasis model regulated by the IL12B/IL23 signaling pathway was analyzed using the IPA database (Fig. 5B). The analysis confirmed the presence of multiple highly correlated IL12B genes and SNP sites, thereby providing substantial evidence to support the existence of the IL-12 pathway.

Figure 3. Bioinformatics network analysis of 1,684 SNP gene locations of the genome-wide association study that are associated with psoriasis, and cross analysis of the number of genes and pathways (SNP gene loci, P<5×10−8). SNP, single nucleotide polymorphism.

Figure 4. Network analysis of single nucleotide polymorphisms associated with psoriasis using genome-wide association study models. (A) Summary of the HLA-related genes, IL-12B and PSORS1 in psoriasis by GWAS analysis (P<5×10−8). (B) Related genetic markers (PSORS1C1, PSORS1C2, IL-12B, TNF, CCHCR1, AGER, DDR1, TUBB, TNIP1, HLA-C, HLA-G and TNXB) associated with psoriasis. GWAS, genome-wide association study; HLA, Human leukocyte antigen; IL-12B, interleukin 12B; PSORS1, psoriasis susceptibility 1; PSORS1C1, psoriasis susceptibility 1 candidate 1; PSORS1C2, psoriasis susceptibility 1 candidate 2; TNF, tumor necrosis factor; CCHCR1, coiled-coil α-helical rod protein 1; AGER, advanced glycosylation end-product specific receptor; DDR1, discoidin domain receptor tyrosine kinase 1; TUBB, tubulin β class I; TNIP1, TNFAIP3 interacting protein 1; HLA-C, major histocompatibility complex, class I, C; HLA-G, major histocompatibility complex, class I, G; TNXB, tenascin XB.

Figure 5. Regional association plot showing signals around chromosomal 5 for the GWAS of psoriasis. (A) Regional association plot of the IL-12B gene in psoriasis. The regional plot showed significant differences in effect-size estimates. The plot typically shows the position of each SNP along the x-axis and negative log10 P-value. The color of the point was used to show the linkage disequilibrium between SNPs. (B) Pathway diagram depicting IL-12B involving genes that are significantly associated with psoriasis (SNPs gene loci, P<5×10−8). GWAS, genome-wide association study; SNP, single nucleotide polymorphism; IL-12B, interleukin 12B; UBLCP1, ubiquitin-like domain-containing CTD phosphatase 1; RNF145, ring finger protein 145.

Table V. Canonical network analysis of GWAS results in psoriasis (P<5×10−8).

Table V.

Canonical network analysis of GWAS results in psoriasis (P<5×10−8).

Ingenuity canonical pathways	-log (P-value)	Molecules
Antigen presentation pathway	27.8	HLA-A, HLA-B, HLA-C, HLA-DMB, HLA-DOB, HLA-DPA1, HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DQB2, HLA-DRB1, HLA-E, HLA-F, HLA-G, PSMB9, TAP1, TAP2
PD-1, PD-L1 cancer immunotherapy pathway	17.7	HLA-A, HLA-B, HLA-C, HLA-DMB, HLA-DOB, HLA-DPA1, HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DQB2, HLA-DRB1, HLA-E, HLA-F, HLA-G, IL12B, TNF
Multiple sclerosis signaling pathway	15.1	C2, HLA-A, HLA-B, HLA-C, HLA-DMB, HLA-DOB, HLA-DPA1, HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DQB2, HLA-DRB1, HLA-E, HLA-F, HLA-G, IL12B, MOG, TNF
IL-10 signaling	13.8	HLA-A, HLA-B, HLA-C, HLA-DMB, HLA-DOB, HLA-DPA1, HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DQB2, HLA-DRB1, HLA-E, HLA-F, HLA-G, TNF
Crosstalk between dendritic cells and natural killer cells	12.7	HLA-A, HLA-B, HLA-C, HLA-DRB1, HLA-E, HLA-F, HLA-G, IL12B, MICA, MICB, NCR3, TNF, HSPA1A/HSPA1B, HSPA1L
Neuroinflammation signaling pathway	12.4	AGER, GABBR1, HLA-A, HLA-B, HLA-C, HLA-DMB, HLA-DOB, HLA-DPA1, HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DQB2, HLA-DRB1, HLA-E, HLA-F, HLA-G, IL12B, TNF
Th1 and Th2 pathway	9.43	HLA-A, HLA-B, HLA-DMB, HLA-DOB, HLA-DPA1, HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DQB2, HLA-DRB1, IL12B, NOTCH4

[i] GWAS, genome-wide association study. PD-1, programmed death 1; PD-L1, programmed cell death-ligand 1; IL-10, interleukin 10; Th, T helper.

PRS model for predicting the risks of psoriasis and associated comorbidities

To analyze the PRSs for psoriasis, the dataset was divided into three sections: Base, target and validation, at a ratio of 8:1:1. Each group was treated as a separate entity. Summary statistics were calculated using data from the base group, and a model was constructed using data from the target group. Data from the validation group were used to assess the accuracy of the model. A total of 1,684 SNPs with a significance level of P<0.001 were chosen from a pool of 865 genes. The raw data for the PRS models are presented in Table SIII. Fig. 6A and B illustrate the PRS distribution and statistical findings applicable to the target and validation groups. Fig. 6C shows the receiver operating characteristic curve of the PRS for the prediction of psoriasis. The data indicated that patients with psoriasis exhibited a significantly higher PRS than those in the control group (P<0.001). Table VI presents an analysis of the area under the curve (AUC) for the PRS for psoriasis. The AUC of the PRS alone model was 0.611 [95% confidence interval (CI), 0.584–0.638], while that of the PRS with age and sex combined was 0.611 (95% CI, 0.584–0.638). In addition, the AUC of PRS with HLA-A*02:07 and HLA-C*06:02 combined was 0.598 (95% CI, 0.571–0.624), while that of PRS combined with age, sex, HLA-A*02:07 and HLA-C*06:02 was 0.629 (95% CI, 0.602–0.656). It was demonstrated that these SNPs collectively represent major risk factors for the development of psoriasis. Furthermore, HLA-A*02:07 and HLA-C*06:02 provided considerable discriminatory ability, making a significant contribution to the risk of psoriasis.

Figure 6. Distribution of the PRS and corresponding statistical results in psoriasis. Distribution of the PRS and corresponding statistical results in the (A) target and (B) validation groups. (C) The ROC curve of the PRS for the prediction of psoriasis. The colors represent different diseases and biomarkers (****P<0.001). ROC, receiver operator characteristic; PRS, polygenic risk score.

Table VI. Area under the curve analysis of PRS in psoriasis.

Table VI.

Area under the curve analysis of PRS in psoriasis.

				Asymptotic 95% confidence interval
			Asymptotic significanceb
Test result variable (s)	Area	Standard errora		Lower bound	Upper bound
PRS	0.611	0.014	0.000	0.584	0.638
PRS_Age_Sex	0.611	0.014	0.000	0.584	0.638
PRS_ HLA-A*02:07_ HLA-C*06:02	0.598	0.014	0.000	0.571	0.624
PRS_Age_Sex_HLA-A*02:07_ HLA-C*06:02	0.629	0.014	0.000	0.602	0.656

{ label (or @symbol) needed for fn[@id='tfn6-mmr-30-1-13239'] } The test result variable (s): PRS_ PSA. PRS has at least one tie between the positive actual state group and the negative actual state group. Statistics may be biased.

a Under the nonparametric assumption;

b null hypothesis: True area =0.5.

Genetic associations between SNPs and various human phenotypes in a PRS model

Psoriasis and various human disorders have been studied using PheWAS. Fig. 7 and Table VII provide an overview of the genetic associations between psoriasis and the other diseases. The three diseases that showed the strongest association with PRS are dermatologic, infectious and musculoskeletal conditions. The present study serves as a crucial clinical benchmark for the treatment of psoriasis. Furthermore, the PheWAS results demonstrated a genetic association between psoriasis and various diseases.

Figure 7. PheWAS analysis of the association between SNPs loci from PRS and human disease (SNPs gene loci, denoting a P<0.001). PheWAS, phenome-wide association; SNP, single-nucleotide polymorphism; PRS, polygenic risk score.

Table VII. PheWAS of SNP loci from PRS in psoriasis.

Table VII.

PheWAS of SNP loci from PRS in psoriasis.

A, Dermatological disease
Phecode	Description	Cases	Controls	P-value	OR
696.4	Psoriasis	1,693	58,813	0.00×1000	Infinity
696	Psoriasis and related disorders	1,753	58,813	0.00×1000	Infinity
696.41	Psoriasis vulgaris	1,518	58,813	0.00×1000	Infinity
696.42	Psoriatic arthropathy	709	58,813	0.00×1000	Infinity
939	Atopic/contact dermatitis due to other or unspecified	4,095	54,969	3.32×10−49	7.63×1069
695	Erythematous conditions	1,316	59,656	1.64×10−37	1.31×10111
706.8	Other specified diseases of sebaceous glands	317	62,272	8.64×10−31	2.09×10188
709.2	Sicca syndrome	212	58,921	3.86×10−30	6.50×10254
690	Erythematosquamous dermatosis	725	59,656	4.23×10−29	2.70×10131
686.5	Pyoderma	675	55,568	1.56×10−28	9.99×10125
690.1	Seborrheic dermatitis	637	59,656	8.22×10−26	3.48×10131
709	Diffuse diseases of connective tissue	527	58,921	2.99×10−23	1.35×10137
706	Diseases of sebaceous glands	1,377	62,272	7.74×10−23	2.20×1078
695.42	Systemic lupus erythematosus	125	59,651	9.71×10−22	1.59×10279
704.8	Other specified diseases of hair and hair follicles	571	63,275	1.16×10−21	1.35×10117
695.4	Lupus (localized and systemic)	168	59,651	3.58×10−20	2.14×10229
686	Other local infections of skin and subcutaneous tissue	2,139	55,568	4.86×10−19	2.30×1057
696.2	Parapsoriasis	47	59,656	1.89×10−17	Infinity
704	Diseases of hair and hair follicles	899	63,275	3.38×10−15	1.30×1077
695.7	Prurigo and Lichen	858	59,656	4.23×10−15	3.68×1083
698	Pruritus and related conditions	415	64,546	1.28×10−10	1.23×1092
701.1	Keratoderma, acquired	138	64,131	7.17×10−9	6.06×10142
706.1	Acne	968	62,272	2.13×10−8	8.69×1052
695.2	Bullous dermatoses	103	59,656	1.09×10−7	2.13×10167
705.1	Dyshidrosis	183	62,272	3.00×10−7	1.53×10111
696.3	Pityriasis	33	59,656	3.81×10−7	1.29×10289
701	Other hypertrophic and atrophic conditions of skin	613	64,131	1.24×10−6	1.44×1057
705	Disorders of sweat glands	221	62,272	3.46×10−6	4.95×1091
681	Superficial cellulitis and abscess	3,667	55,568	4.48×10−6	5.32×1022
695.3	Rosacea	94	59,656	6.48×10−6	4.13×10147
695.22	Pemphigus and pemphigoid	61	59,656	1.35×10−5	4.93×10178
703	Diseases of nail, NOS	60	63,275	1.63×10−5	3.17×10161
947	Urticaria	1,624	54,969	9.10×10−10	1.95×1045
B, Infectious diseases
Phecode	Description	Cases	Controls	P-value	OR
110.1	Dermatophytosis	1,439	61,270	6.03×10−29	1.31×1087
110	Dermatophytosis/Dermatomycosis	1,585	61,270	1.09×10−26	3.43×1079
110.12	Athlete's foot	770	61,270	3.09×10−19	2.38×1095
110.11	Dermatophytosis of nail	621	61,270	4.59×10−18	3.57×10102
110.13	Dermatophytosis of the body	406	61,270	7.40×10−17	5.03×10121
78	Viral warts and HPV	737	50,808	4.44×10−7	4.35×1054
53	Herpes zoster	932	50,808	5.32×10−7	2.23×1048
54	Herpes simplex	554	50,808	1.17×10−6	6.30×1060
C, Musculoskeletal disease
Phecode	Description	Cases	Controls	P-value	OR
714.1	Rheumatoid arthritis	156	60,426	1.01×10−26	3.32×10279
715.2	Ankylosing spondylitis	124	60,426	1.36×10−15	1.38×10232
714	Rheumatoid arthritis and other inflammatory polyarthropathies	540	60,426	1.42×10−14	6.14×10103
715	Other inflammatory spondylopathies	182	60,426	1.42×10−12	1.21×10167
721.8	Other allied disorders of spine	98	57,268	4.74×10−10	1.78×10180
740.1	Osteoarthritis; localized	2,164	59,489	7.61×10−8	1.16×1034
740.11	Osteoarthrosis, localized, primary	1,624	59,489	1.05×10−7	5.91×1038
716	Other arthropathies	1,082	61,357	2.09×10−6	2.64×1042
D, Endocrine/metabolic disease
Phecode	Description	Cases	Controls	P-value	OR
242	Thyrotoxicosis with or without goiter	1,743	58,192	2.79×10−7	1.63×1036
279.1	Immunity deficiency	101	64,597	1.13×10−6	1.48×10140
250.1	Type 1 diabetes	354	53,008	4.32×10−6	1.98×10−69
250.11	Type 1 diabetes with ketoacidosis	90	53,008	8.82×10−6	2.91×10−128
279	Disorders involving the immune mechanism	563	64,597	1.61×10−5	9.35×1052
E, Circulatory system disease
Phecode	Description	Cases	Controls	P-value	OR
446.3	Hypersensitivity angiitis	49	63,133	2.64×10−6	1.59×10191
446	Polyarteritis nodosa and allied conditions	172	63,133	3.41×10−6	1.18×10103
F, Hematopoietic disease
Phecode	Description	Cases	Controls	P-value	OR
289	Other diseases of blood and blood-forming organs	264	62,279	1.04×10−6	1.13×1079
286.81	Primary hypercoagulable state	144	64,206	3.22×10−6	7.74×10100

[i] OR, odds ratio; PheWAS, phenome-wide association study; SNP, single nucleotide polymorphism; PRS, polygenic risk score; HPV, human papillomavirus.

Meta-analysis by the BBJ

Finally, a meta-analysis was conducted in collaboration with the BBJ to investigate the genetic relationships within the East Asian population with respect to psoriasis. The psoriasis GWAS from Biobank Japan (BBJ) was published previously (29). The summary statistics of psoriasis were downloaded from BBJ to replicate our results. Table SIV presents unprocessed results from the meta-analysis of SNPs. Upon completion of the meta-analysis, the top 20 SNPs retained their genome-wide significance (Table VIII). In the BBJ cohort, results identified 438 significant single nucleotide polymorphisms associated with psoriasis, with a significance level of P<1×10−5. The majority of these SNPs are located on chromosome 6. By conducting a meta-analysis of the GWAS at key SNP sites within the BBJ Cohort, the persistent significance of these SNPs wAS confirmed. This validates the robustness and reliability of the present findings.

Table VIII. Meta-analysis of SNP loci on psoriasis in CMUH and BBJ studies.

Table VIII.

Meta-analysis of SNP loci on psoriasis in CMUH and BBJ studies.

				CMUH			BBJ			Meta-analysis
CHR	SNP	REF	ALT	β	SE	P-value	β	SE	P-value	β	SE	P-value
6	rs1634774	A	G	0.494	0.034	7.19×10−47	0.366	0.114	0.001	0.483	0.033	7.36×10−49
6	rs9380151	T	C	0.609	0.044	2.40×10−43	0.958	0.244	8.55×10−5	0.620	0.043	2.79×10−46
6	rs9368611	A	C	0.621	0.045	3.09×10−43	1.354	0.299	5.78×10−6	0.637	0.045	1.95×10−46
6	rs75791723	C	T	0.605	0.045	5.48×10−42	1.235	0.267	3.58×10−6	−0.623	0.044	1.73×10−45
6	rs4997405	C	T	0.465	0.035	3.00×10−41	−0.137	0.099	0.165	−0.400	0.033	1.97×10−34
6	rs2596521	A	G	0.456	0.034	6.38×10−41	0.467	0.103	5.68×10−6	0.457	0.032	2.05×10−45
6	rs9391847	A	G	0.456	0.034	9.99×10−41	0.475	0.103	4.04×10−6	0.458	0.032	2.34×10−45
6	rs2523624	G	A	0.454	0.034	1.05×10−40	0.467	0.103	5.62×10−6	−0.456	0.032	3.33×10−45
6	rs3132482	G	A	−0.463	0.035	1.75×10−39	−0.085	0.099	0.393	0.421	0.033	7.72×10−37
6	rs9263986	A	G	0.453	0.034	1.86×10−39	0.014	0.102	0.889	0.408	0.033	7.94×10−36
6	rs3868082	A	G	−0.453	0.035	1.15×10−38	−0.146	0.099	0.138	−0.419	0.033	2.84×10−37
6	rs9393991	G	A	0.576	0.044	1.70×10−38	0.968	0.247	9.22×10−5	−0.589	0.044	2.63×10−41
6	rs117565607	T	A	0.577	0.045	2.51×10−38	1.092	0.265	3.66×10−5	−0.591	0.044	3.04×10−41
6	rs6927080	C	G	0.444	0.034	7.28×10−38	0.026	0.102	0.797	0.401	0.033	1.42×10−34
6	rs9391681	T	C	0.578	0.045	8.59×10−38	1.279	0.286	7.57×10−6	0.595	0.045	6.91×10−41
6	rs117416277	T	C	0.573	0.045	1.53×10−38	0.968	0.237	4.44×10−5	0.586	0.044	1.35×10−40
6	rs3132484	G	T	−0.462	0.036	2.64×10−37	−0.105	0.101	0.296	0.421	0.034	3.96×10−35
6	rs3132483	G	A	−0.462	0.036	2.64×10−37	−0.105	0.101	0.296	0.421	0.034	3.96×10−35
6	rs3132485	C	A	−0.462	0.036	2.65×10−37	−0.106	0.101	0.294	0.421	0.034	3.93×10−35
6	rs12194291	G	C	0.545	0.043	3.36×10−37	0.957	0.214	7.81×10−6	−0.561	0.042	8.70×10−41

[i] CHR, chromosome; POS, position; REF, reference allele; ALT, alternative allele; CMUH, China Medical University Hospital; BBJ, Biobank Japan; SNP, single nucleotide polymorphism; SE, standard error.

Discussion

Psoriasis is a complex disease involving various pathogenic and immunological mechanisms (17,96). Although >100 psoriasis susceptibility loci have been identified through GWAS in different countries and ethnicities (96–101), there have been limited studies on genetic susceptibility loci in Taiwan (102,103). The present study conducted GWAS and PRS to identify 92 novel genomic markers and identified 61 previously reported genomic markers associated with psoriasis. Through network analysis, several biological processes were discovered that contribute to psoriasis. These processes involve factors such as HLA subtypes and genetic SNPs that affect cytokines, and the PSORS1 locus. Additionally, it is possible that transcriptional regulators and shared pathogenic processes as well as other human diseases are involved in the development of psoriasis.

The immune response plays an important role in the development of psoriasis, and HLA polymorphisms have been implicated in this process (104,105). Major histocompatibility complex (MHC) molecules are present on every cell and provide peptide antigens to CD4+ and CD8+ T cells (106,107). The length of these antigens is usually 8–10 amino acids (108). The presence of HLA polymorphisms leads to variable peptide-binding grooves that contain two or three specific acceptor sites or pockets (109). HLA molecules present a variety of antigenic peptides by binding to specific amino acid side chains (110). The acceptor sites on HLA molecules differ from one another, resulting in different peptide repertoires being presented for different HLA molecules (109,110). The antigenic peptides contained in each HLA molecule are unique, despite some overlap in binding specificities (111,112). Certain HLA alleles, such as HLA-A (61), HLA-B (62,63), HLA-C (53,54), HLA-DMB (64), HLA-DQA1 (65), HLA-DQA2 (66), HLA-DQB1 (67), HLA-DRB1 (67), HLA-E (68), HLA-F (69) and HLA-G (70), are more prevalent in patients with psoriasis. The findings of the present study indicated a significant association between the genetic variability of HLA-related genes located on chromosome 6 and the development of psoriasis. It was confirmed that certain HLA genes, including HLA-DPA1, HLA-E, HLA-F, HLA-G, MICA and MICB have amino acid mutations (68,113–115). The findings from the present study indicated a notable association between HLA-A*02:07 and HLA-C*06:02 alleles and psoriasis in the Taiwanese population. The HLA-C*06:02 allele is the most prominent risk factor for psoriasis (116–119). The risk allele frequencies of psoriasis have been reported to be 6–17% in Taiwan, 8–26% in Japan, 76.1% in Korea, 14% in Thailand and 46–67% in Caucasian populations (120). Shen et al demonstrated a significant association between psoriasis and certain HLA alleles, including HLA-A*02:07 and HLA-C*06:02, in Chinese patients with psoriasis in Singapore (121). Furthermore, the HLA-A*02:07 allele is significantly associated with the development of psoriasis in individuals residing in southern China (122). In Japan, psoriasis vulgaris has been reported to be associated with HLA-A*02:07 and HLA-C*06:02 alleles (123). The HLA-Cw*06 allele is associated with the highest degree of susceptibility to psoriasis (116,124,125). It associates with the occurrence of psoriasis as well as with improved treatment outcomes with methotrexate, IL-12, IL-17 and IL-23 targeted therapeutic agents (126). Furthermore, HLA-C*06:02 has been identified as a biomarker for predicting the response to biological treatment in patients with psoriasis (116). The present study also suggested that HLA-A*02:07 and HLA-C*06:02 significantly contribute to the risk of psoriasis, as demonstrated by the PRS analysis.

Pro-inflammatory cytokines, including IL-12, IL-23 and TNF, play crucial roles in the development and progression of psoriasis (72). IL-12B/IL-23 and TNF are involved in inflammatory processes (72). IL-12B encodes for a subunit of IL-12. IL-12 is a disulfide-linked heterodimer consisting of two subunits: A 40 kD cytokine receptor-like subunit encoded by IL-12B and a 35 kD subunit encoded by IL-12A (127). IL-12 exerts its effects on T and natural killer cells, leading to the activation of these immune cell populations (128). It is expressed by activated macrophages, which play a crucial role in Th1 cell development. IL-12 is essential for maintaining an adequate number of memory/effector Th1 cells to provide long-term protection against intracellular pathogens (129). IL-23 cytokines are composed of two subunits, IL-23A and IL-12B, both of which play significant roles in various biological functions (127). IL-12B and IL-23 are particularly important in the differentiation of T cells, specifically, the generation of Th1 cells that produce IFN-γ and Th17 cells that produce IL-17 (72). Previously, it was observed that IL-12 is overexpressed on dendritic cells in the skin lesions of patients with psoriasis (130). Moreover, ustekinumab, an FDA-approved monoclonal antibody targeting IL-12/23p40, has shown great efficacy in treating patients with psoriasis (99,131). In the present study a strong association between variations in IL-12B levels was identified in the psoriasis group. Previous studies identified several SNPs associated with psoriasis in genes such as IL-12 (rs3212220, rs3212217 and rs3212227), IL-23/IL-23R and IL-17 (89,132). However, the present study did not find any polymorphisms at the SNP loci of IL-23 or IL-17. Overall, our findings suggested that IL-12B and its associated SNPs on chromosome 5 are important in the pathogenesis of psoriasis. Further investigation of these pathways may provide valuable insights into the mechanisms underlying the disease.

Previous studies have shown that HSPA1A/HSPA1B and TRIM15 play roles in the pathogenesis of psoriasis (71,91). HSPA1A and HSPA1B are heat shock proteins that regulate the secretion of TNF-α, IL-1β and IL-10 from monocytes (133). TRIM15, on the other hand, stimulates TNF-α and is involved in the TNF-α/NF-κB pathway, contributing to the inflammatory response (91). In the present study psoriasis was associated with SNPs in TNF (rs1800629) and TNIP1 (rs76956521, rs2233278, rs75851973 and rs8177833). Our study established a connection between HSPA1A/HSPA1B, TRIM15 and psoriasis. Additionally, several genes associated with enzymes and kinases were identified including AGPAT1, ATAT1, BAG6, CARMIL1, DHX16, DDAH2, GPX5, GPX6, NEU1, PGBD1, PPP1R11, SKIC2, VARS1 and VARS2, as SNP loci. Currently, there is no definitive evidence linking enzymatic activity with psoriasis, which requires further investigation. Future studies should, therefore, prioritize research on these genes.

Familial recurrence of psoriasis has been extensively studied, and it has been found that monozygotic twins are more likely to have the disease than dizygotic twins (134). The main genetic factor responsible for psoriasis is PSORS1, which is located within the MHC on chromosome 6p21.3 (135,136). This region spans a range of 80–250 kb (137). The PSORS1 locus contains several genes including HLA-C, MICA, PSORS1C3 and CDSN. These candidate genes have alleles within the PSORS1 locus and have been shown to be expressed in skin cells (138). HLA-C is responsible for presenting peptides to cytotoxic T cells (CD8+T cells) by interacting with them on the cell membrane, which activates the immune response and cytotoxicity of cytotoxic T cells (139). MICA is a cell-surface glycoprotein encoded by the MICA gene located within the MHC locus. It is recognized by NK cells, γδ T cells and CD8+ αβ T cells, which carry the NKG2D receptor on their cell surfaces (140). CDSN is primarily expressed in the upper layers of epidermis and hair follicles. It contributes to keratinocyte cohesion, and is targeted by proteases during epidermal desquamation (141). PSORS1C3 is a non-coding gene and its RNA transcript is found in patients with psoriasis. The functional role of PSORS1C3 is to modulate the inflammatory response (142). An association between PSORS1C3 polymorphisms and psoriasis has been reported in various populations. Additionally, the specific alleles of HLA-Cw*0602 and CDSN*5 consistently demonstrated a significant association with psoriasis (142,143). Tawfik et al (144) conducted a study that showed an association between psoriasis and three variants (rs10484554, rs887466 and rs1062470), within the PSORS1 locus. These variants are associated with LOC105375015, PSORS1C3 and PSORS1C1 (137). Additionally, GWAS identified 36 regions that contributed to psoriasis susceptibility. However, >50% of the genetic variance is attributed to a single MHC locus, specifically PSORS1 (145). One of the candidate genes in psoriasis is HLA-C, as demonstrated by a GWAS performed using markers linked to HLA-Cw*0602 (145). However, some studies have suggested that PSORS1 does not play a role in the development of late-onset psoriasis (143,146,147). In the present study, a connection between psoriasis and specific genetic markers was discovered, namely, PSORS1C1, PSORS1C2, PSORS1C3, MICA and CDSN. These genes have been linked to various factors, such as human keratinocyte differentiation (53,148–150), the inflammatory response (112,151–153) and in medication toxicity, particularly the Stevens-Johnson syndrome associated with allopurinol (142,154,155).

In Taiwan, few studies have been conducted on the use of PRS for the treatment of psoriasis. Evidence suggests that diabetes mellitus is increasingly prevalent among patients with psoriasis (22,156,157). Eiris et al (158) showed a significant association between three SNPs (rs6887695, rs3212227 and rs2201841) and diabetes mellitus. Recent GWAS findings have indicated a genetic association between psoriasis and autoimmune diseases (such as multiple sclerosis, rheumatoid arthritis and autoimmune hypothyroidism), neuromuscular diseases (including Alzheimer's and Parkinson's diseases) (159–162), chronic inflammation (163) and skin diseases (164). Future investigations will aim to conduct a transdisease meta-analysis and Mendelian randomization to determine the causal relationship between psoriasis and the aforementioned diseases. It is important to note that the present study was subject to numerous limitations, including the risk of false positives and false negatives. It cannot be confirmed whether participants in the present were diagnosed with psoriasis or other autoimmune diseases at different hospitals, leading to such limitations in the present study. Additionally, due to the reliance on retrospective medical record reviews for selecting the experimental and control groups, accurate classification of the severity of psoriasis could not be performed. It is also hypothesized that the severity of psoriasis is highly associated with genetics. Therefore, incorporating severity into future studies will likely significantly enhance the efficacy of the PRS model.

In conclusion, the present study has made significant discoveries regarding psoriasis in Taiwan. This confirms the influence of genetic variation and susceptibility on the development of psoriasis. Loci such as the HLA region, PSORS1 and IL-12B were identified that are strongly associated with psoriasis. The findings also showed an association between specific alleles, such as HLA-A*02:07 and HLA-C*06:02, and HLA genotypes, highlighting the role of genetic factors. Analysis of the PRS indicates that the location of SNPs can accurately predict psoriasis occurrence. Fig. 8 shows the importance of pathological mechanisms and signaling pathways in psoriasis development, as shown by the GWAS results of the present study. Importantly, the present study is the first to link multiple genetic loci in Taiwanese individuals with the onset of psoriasis. Overall, the present research emphasizes the critical role of genetic factors and signaling pathways in psoriasis development, providing valuable insights for future investigations and potential therapeutic interventions.

Figure 8. Molecular mechanisms involved in psoriasis are depicted. The damage to keratinocytes and the release of antigens are caused by genetic variables (PSORS1, AGER, DDR1, TUBB, TNIP1 and TNXB) and environmental factors. At point (A), CD8+ T cells are activated by APCs such as macrophages and dendritic cells that present the antigen through HLA-A, HLA-B and HLA-C. At point (B), APCs deliver antigens to CD4+ T cells through HLA-D, which stimulates the differentiation of CD4+ T cells into Th17, Th1 and Th22 cells. IFN-γ, IL-17 and IL-22 are also secreted. At point (C), Myeloid dendritic cells release the cytokines IL-23, IL-12B and TNF-α, which stimulate the growth of Th17, Th1 and Th22 cells. The cells also secrete IL-17, IL-22 and IFN-γ. As a result of the aforementioned findings, keratinocytes and endothelial cells are activated and grow excessively. In psoriasis, tissues are reconfigured, chemokines are produced, the immune system is over-activated, vascular proliferation occurs and neovascularization occurs. PSORS1, psoriasis susceptibility 1; AGER, advanced glycosylation end-product specific receptor; DDR1, discoidin domain receptor tyrosine kinase 1; TUBB, tubulin β class I; TNIP1, TNFAIP3 interacting protein 1; TNXB, tenascin XB; APCs, antigen-presenting cells; HLA-A, major histocompatibility complex, class I, A; HLA-B, major histocompatibility complex, class I, B; HLA-C, major histocompatibility complex, class I, C; HLA-D, major histocompatibility complex, class II, D; Th17 cells, T helper 17 cells; Th1 cells, T helper 1 cells; Th22 cells, T helper 22 cells; IFN-γ, Interferon γ; IL-17, interleukin 17; IL-12, interleukin 12; IL-12B, interleukin 12B; IL-22, interleukin 22; IL-23, interleukin 23; TNF, tumor necrosis factor.

Supplementary Material

Supporting Data

Supporting Data

Supporting Data

Supporting Data

Acknowledgements

The authors would like to thank the Office of Research and Development, China Medical University (Taichung, Taiwan) for providing Medical Research Core Facilities to perform the experiments and data analysis. The authors also thank Dr Kuan-Wen Chen and Dr Yao-Wei Jheng from GGA Corporation's Molecular Science and Digital Innovation Center in Taipei, Taiwan, who made contributions to the biological significance of the figures and tables presented in this paper through their in-depth analysis and interpretation. Their analysis was instrumental in accurately deciphering the data.

Funding

This work was supported in part by China Medical University Hospital, Taiwan (grant no. DMR-113-109) and The Ministry of Science and Technology, Taiwan (grant no. MOST 111-2314-B-075-083-MY2).

Availability of data and materials

The data generated in the present study are included in the figures and/or tables of this article. The data generated in the present study may be found at the following URL: https://my.locuszoom.org/gwas/590520/?token=2ea84c806f5f46aea3dcb3fb1ad21f99.

Authors' contributions

JSY, TYL and FJT were responsible for the overall conception and design. JSY, TYL and HFL performed the acquisition of data. TYL, JSY and YWW performed the GWAS, PRS and PheWAS analyses. TYL, HFL and WLL performed the interpretation of GWAS, PRS and PheWAS results. SCT, JSY and YJC performed the analysis of the bioinformatics network and interpreted the data. TYL and WLL assessed the HLA diplotypes, and performed the allele frequency analysis and interpretation of data. HFL and YWW performed the meta-analysis and interpretation of data. YJC and WLL performed the interpretation of clinical pathological mechanisms. JSY and FJT confirm the authenticity of all the raw data. All authors have read and approved the final version of the manuscript.

Ethics approval and informed consent

The study protocol was approved by the Institutional Review Board of China Medical University Hospital and categorized as the Precision Medicine Project (CMUHPMP) (IRB number: CMUH110-REC3-005 and CMUH111-REC1-176). Patients have been granted access to their medical records by the CMUH IRB. The CMUH IRB also places considerable emphasis on ensuring patient confidentiality. De-identified genetic and clinical data were collected after obtaining informed consent from patients. The present study complied with The Declaration of Helsinki.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References