Genome-wide meta-analysis identifies significant vitiligo loci
A meta-analysis of vitiligo GWAS data from studies by Jin et al., the FinnGen cohort, and the UK Biobank (Supplementary Table 1) identified variants at a genome-wide significance level (p < 5 × 10− 8) (Fig. 1). The quantile-quantile (Q-Q) plot of the meta-analysis is shown in Supplementary Fig. 1. We annotated potential causal genes for newly discovered vitiligo variants and conducted enrichment analysis of these variants. Additionally, we explored the associations and genetic correlations between these variants and seven vitiligo risk factors.
After quality control, the meta-analysis of vitiligo GWAS data from three studies yielded results for 14,608,392 genetic variants associated with vitiligo. Twenty-five variants showed genome-wide significant signals, six of which were located more than 100 Kb from previously reported index variants (Fig. 2 and Table 1).
Gene mapping of significant vitiligo genetic variants
We performed fine-mapping using GWAS summary statistics (Supplementary Fig. 2). Using FUMA, we mapped genes within a 500 Kb region of the index SNPs and identified genes with the highest Polygenic Priority Score (PoPS) in this region (Table 1). Notably, rs201529506 did not index any annotated genes within the 500 Kb region.
Table 1
Loci identified in the GWAS meta-analysis of vitiligo
rsID | Effect allele | Other allele | EAF | N | Beta | SE | P value | PoPs gene | PoPs score |
Previously reported loci | | | | | | | | | |
rs11453006 | D | I | 0.9 | 44266 | 0.446 | 0.060 | 1.17E-15 | TUBB3 | 0.289 |
rs35059390 | D | I | 0.53 | 44266 | 0.191 | 0.030 | 3.29E-11 | TG | 0.928 |
rs34976449 | D | I | 0.56 | 44266 | 0.199 | 0.030 | 5.38E-12 | RERE | 0.551 |
rs11434358 | D | I | 0.61 | 44266 | 0.174 | 0.030 | 1.75E-09 | TICAM1 | 0.629 |
rs9981980 | T | G | 0.67 | 44266 | 0.285 | 0.030 | 2.51E-22 | UBASH3A | 0.222 |
rs148136154 | T | C | 0.88 | 44266 | -0.315 | 0.040 | 6.89E-15 | CD80 | 0.455 |
rs58473363 | D | I | 0.6 | 44266 | -0.207 | 0.030 | 9.84E-13 | CD44 | 0.941 |
rs113759362 | A | G | 0.98 | 44266 | 0.577 | 0.090 | 1.44E-11 | HLA-DRB1 | 0.805 |
rs9279765 | D | I | 0.76 | 44266 | -0.357 | 0.040 | 1.81E-23 | HLA-DRB1 | 0.805 |
rs60949565 | D | I | 0.94 | 44266 | -0.358 | 0.050 | 5.27E-12 | HERC2 | 0.100 |
rs61710173 | D | I | 0.52 | 44266 | -0.207 | 0.030 | 6.04E-13 | CCR6 | 0.371 |
rs78567876 | A | T | 0.55 | 44266 | 0.531 | 0.030 | 1.34E-75 | HLA-DRB1 | 0.805 |
rs35663695 | D | I | 0.69 | 44266 | 0.315 | 0.030 | 2.88E-21 | NOX4 | -0.123 |
rs28520266 | C | G | 0.5 | 44266 | -0.211 | 0.030 | 2.43E-13 | IFIH1 | 0.465 |
rs141105463 | D | I | 0.58 | 44266 | 0.223 | 0.030 | 6.75E-14 | FOXP1 | 0.624 |
rs113284964 | D | I | 0.67 | 44266 | 0.199 | 0.030 | 2.08E-11 | RHOH | 1.424 |
rs10676540 | D | I | 0.79 | 44266 | 0.223 | 0.040 | 2.90E-09 | TOB2 | 0.365 |
rs145282249 | A | T | 0.55 | 44266 | -0.278 | 0.030 | 7.74E-22 | LPP | 0.588 |
rs67822292 | T | G | 0.9988 | 337159 | 0.004 | 0.001 | 3.01E-09 | NA | NA |
Novel loci | | | | | | | | | |
rs114259595 | A | G | 0.62 | 44266 | 0.166 | 0.030 | 8.27E-09 | BACH2 | 0.595 |
rs79838180 | T | G | 0.62 | 44266 | 0.236 | 0.030 | 5.61E-15 | ANKRD11 | 0.353 |
rs5845323 | D | I | 0.57 | 44266 | -0.285 | 0.030 | 3.77E-23 | NCF4 | 0.462 |
rs142543923 | D | I | 0.95 | 44266 | -0.392 | 0.060 | 5.77E-13 | CCDC88A | 0.208 |
rs201529506 | A | C | 0.6348 | 544772 | -0.001 | 0.000 | 4.09E-08 | NA | NA |
rs2017445 | A | G | 0.6185 | 251879 | 0.265 | 0.029 | 2.76E-09 | BLOC1S1 | 0.321 |
EAF, effect allele frequency; SE, standard error; PoPs, Polygenic Priority Score.
Firstly, we conducted enrichment analysis using genes within the 500 Kb region of the index SNPs. GO analysis showed biological pathways enriched in immune-related pathways such as regulation of leukocyte cell-cell adhesion and antigen processing and presentation. KEGG analysis indicated that genes are associated with autoimmune diseases like autoimmune thyroid disease and Type I diabetes mellitus (Fig. 3, A and B). Subsequently, using the 25 vitiligo GWAS variants as cis-eQTLs in GTEx v8, we identified 38 significantly associated unique genes (p < 5 × 10− 4) and performed further GO and KEGG analyses. The results revealed biological pathways including neutrophil aggregation, leukocyte aggregation and chemotaxis, and KEGG pathways like complement and coagulation cascades and the IL-17 signaling pathway (Fig. 3, C and D).
PoPS-suggested genes were used to characterize different variants. rs9279765 (HLA-DRB1) showed the most significant association values with autoimmune thyroiditis (p = 4.9 × 10− 16) and rheumatoid arthritis (p = 2.5 × 10− 9). Additionally, rs2017445 (BLOC1S1) was associated with rheumatoid arthritis (p = 4.3 × 10− 4), rs10676540 (TOB2) with insomnia (p = 2.6 × 10− 3), and rs5845323 (NCF4) with autoimmune thyroiditis (p = 3.2 × 10− 3). Vitiligo variants showed the most associations with autoimmune thyroiditis, and the direction of association was consistent with the risk of vitiligo. No associations were found between variants and depression, anxiety, alcohol dependency, or smoking dependency (Fig. 3E).
Using LDSC regression analysis, we assessed the genetic correlation between seven vitiligo risk factors and vitiligo. As shown in Supplementary Fig. 2, LDSC indicated no suggestive correlation between the seven vitiligo risk factors and vitiligo (p > 0.05).
MR and SMR identify immune response genes for vitiligo
From the cis-pQTLs of immune response genes extracted from seven plasma proteome GWAS cohorts, 381 protein-SNP pairs were utilized as instrumental variables (Table S3) in a two-sample MR analysis with GWAS meta-analysis. Using the Wald ratio or IVW methods, 30 immune response proteins were significantly associated with vitiligo risk (FDR < 0.05) (Table S4, Fig. 4, A and B). To further validate the observed results, SMR analysis was also performed on the included immune response proteins, revealing 100 proteins significantly associated with vitiligo risk (FDR < 0.05) (Table S8). Eight proteins passed both MR and SMR tests. Elevated predicted levels of CXCL6 were associated with increased vitiligo risk, whereas other proteins (CDH17, LEAP2, CRP, IL9, CFHR5, CD160, and TYRO3) were negatively correlated with vitiligo risk (Table 2). SMR plots and effect diagrams for these eight proteins are displayed in Supplementary Figs. 3 and 4.
Table 2
Summary results from Mendelian randomization (MR) and SMR for 8 proteins passing all tests
Protein | Method | Number of SNPs | MR | | | SMR | | |
| | | Beta | SE | FDR | Beta | SE | FDR |
CD160 | Wald ratio | 1 | -0.0092 | 0.0008 | 0.0000 | 0.0152 | 0.0061 | 0.0431 |
CRP | Wald ratio | 1 | -0.0125 | 0.0034 | 0.0397 | 0.0176 | 0.0027 | 0.0000 |
CFHR5 | Wald ratio | 1 | -0.0027 | 0.0003 | 0.0000 | 0.0133 | 0.0003 | 0.0000 |
CXCL6 | Wald ratio | 1 | 0.0039 | 0.0004 | 0.0000 | 0.0053 | 0.0005 | 0.0000 |
LEAP2 | Wald ratio | 1 | -0.0133 | 0.0005 | 0.0000 | -0.0107 | 0.0018 | 0.0000 |
IL9 | Wald ratio | 1 | -0.0192 | 0.0033 | 0.0000 | -0.0308 | 0.0064 | 0.0000 |
CDH17 | Wald ratio | 1 | -0.0113 | 0.0014 | 0.0000 | -0.0114 | 0.0015 | 0.0000 |
TYRO3 | Wald ratio | 1 | -0.0093 | 0.0003 | 0.0000 | 0.0200 | 0.0082 | 0.0485 |
SNP, single nucleotide polymorphisms; MR, Mendelian randomization; SMR, summary-based Mendelian randomization; SNP, single nucleotide polymorphisms; SE, standard error; FDR, false discovery rate.
In sensitivity analysis (limited to MR), mixed pQTLs (cis + trans) for these eight proteins were reselected for MR. Excluding CRP and IL9, the remaining six maintained significant associations with vitiligo risk (FDR < 0.05) using the Wald ratio or IVW methods, consistent with the direction in cis MR (Table S5). In the cis + trans MR, heterogeneity evidence based on the Cochran Q statistic in the IVW model was observed for CRP, IL9, and CDH17 (Pheterogeneity > 0.05). No horizontal pleiotropy was detected through MR-Egger intercept tests. All proteins' relationship with vitiligo was confirmed by Steiger directionality tests (Table S5).
In subtype analyses of vitiligo, all eight proteins passed replication MR analysis, showing associations with both early-onset and late-onset types (FDR < 0.05) (Tables S6 and S7). The results for CRP, CHFR5, CD160, and TYRO3 were directionally consistent across vitiligo and its subtypes.
Integrating multi-omics and tissue-specific evidence
Our aim was to integrate multi-omics evidence to identify immune response genes among the eight candidate proteins significantly associated with vitiligo risk at gene expression and methylation levels.
CD160 gene expression correlated with vitiligo risk at an FDR < 0.05 level. Higher predicted levels of CD160 (β = 0.010, se = 0.002) were positively associated with vitiligo risk (Table S9).
Results for the causal effects of methylation in immune response genes on vitiligo are stored in Table S10. After correcting for multiple tests, we identified nine CpG sites near two immune response genes, including CD160 (cg06095777, cg11743829, cg25221984, cg08614201, cg20975414, and cg12832565), and TYRO3 (cg24374636, cg15330654, and cg25906537) (Table S). The direction of effect estimates at different CpG sites within the same gene was not always consistent. For example, an increase in predicted methylation of CD160 at cg25221984 by one standard deviation was associated with decreased vitiligo risk (β = -0.003, se = 0.001), whereas increases at other CpG sites were associated with increased risk (Table S10).
We further explored whether the identified proteins in blood could replicate similar causal relationships in skin tissue. No significant associations were found between identified immune response genes and vitiligo risk in skin tissue. However, we identified several skin tissue-specific immune response genes associated with vitiligo risk (FDR < 0.05), which also met the FDR < 0.05 level in plasma protein MR or SMR analyses, including GNLY, IL1R2, TDGF1, MASP1, C6, SFTPD, PLCG2, and MIF (Table S11).
Phenome-wide association analysis
We evaluated the causal relationships between eight MR proteomic genes and seven vitiligo risk factors to explore potential mediation in their association with vitiligo risk.
We observed that CFHR5 (β = -0.0008, se = 0.0004) and IL9 (β = 0.004, se = 0.002) were associated with anxiety or panic attacks, and the direction of association was consistent. No causal relationships were observed between the remaining proteins and the seven vitiligo risk factors (Fig. 4C).
Additionally, for the eight MR proteomic genes, we searched the EpiGraphDB database and identified 62 reported associations with medical terms related to vitiligo, except for CDH17, IL9, and CD160, which were not listed in the database. The most frequently occurring terms were severe depression, bipolar disorder, and schizophrenia (Table S12).
Cell type-specific expression in skin tissue
We downloaded scRNA-seq data from five healthy controls and ten vitiligo patients from the GSA, totaling 48,887 cells after merging the data. Clusters were annotated as eight cell types based on gene markers, including keratinocytes, melanocytes, fibroblasts, endothelial cells, smooth muscle, T & NK cells, langerhans cells, and mononuclear phagocytes (M. phagocytes) (Fig. 5, A and B). The relative abundance of the eight main cell groups across 15 samples and two groups is shown in Fig. 5C. We found that the proportion of immune cells increased in vitiligo patients compared to healthy controls, while the numbers of keratinocytes and melanocytes decreased and the rest increased.
In the top five genes with the highest PoP scores mapped from newly discovered vitiligo GWAS variants, as well as the eight proteins related to vitiligo, CRP, CFHR5, and IL9 were not detected in the scRNA-seq data. BACH2, CCDC88A, BLOC1S1, NCF4, ANKRD11, CD160, LEAP2, CDH17, and TYRO3 were expressed in the skin tissue, while CXCL6 was not detected. Compared to healthy controls, CCDC88A, BLOC1S1, NCF4, LEAP2, and CDH17 showed increased expression in vitiligo lesions, whereas BACH2, ANKRD11, CD160 and TYRO3 showed reduced levels (Fig. 5D). Additionally, some genes showed cell type-specific enrichment in vitiligo lesions. BACH2, CD160, and CDH17 were specifically enriched in T & NK cells, LEAP2 showed increased expression in langerhans cells, and TYRO3 was specifically enriched in melanocytes (Fig. 5E). CXCL6 and CDH17 were not detected in normal skin tissue (Supplementary Fig. 5A).
We further investigated the heterogeneity of gene expression in T & NK subgroups. We extracted T & NK from the scRNA-seq dataset and identified six subgroups with distinct gene expression profiles through clustering and annotation, including five T cell clusters and one NK cell cluster. T cell clusters were divided into CD4 + Teff, CD4 + Treg, CD8 + Tem, CD8 + Trm, and CD8 + Tex (Fig. 6A). Compared to healthy controls, the percentages of CD8 + Tem, CD8 + Trm, and CD8 + Tex increased in vitiligo, while CD4 + Teff decreased (Fig. 6B). Each T cell subgroup exhibited enrichment in different biological processes (Supplementary Fig. 5C). We found that BACH2 and CDH17 were enriched in CD8 + Tex and upregulated in vitiligo. CD160 was enriched in NK cells and downregulated in vitiligo (Fig. 6C and Supplementary Fig. 5, D and E). Pseudotime analysis revealed CD8 + Tex and NK in the terminal stages of T cell differentiation (Supplementary Fig. 5F), indicating that BACH2, CD160, and CDH17 expression levels increased during T cell differentiation, with changes in CDH17 appearing in later stages (Fig. 6D).
Similarly, we performed further clustering and annotation of myeloid cells from the samples, identifying six subgroups: three mononuclear phagocyte subgroups (Mφ_CXCL8, Mφ_APOE, and Mono_FCN1) and three langerhans subgroups (LC1, LC2, and aLC) (Fig. 6E). The number of myeloid cell subtypes increased to varying degrees in vitiligo, particularly Mφ_APOE (Fig. 6F). Each myeloid cell subgroup showed enrichment in different biological processes (Supplementary Fig. 5H). We found that CCDC88A was highly expressed in Mφ_CXCL8 and Mono_FCN1, BLOC1S1 was highly expressed in Mono_FCN1, LC1, and LC2, and NCF4 was highly expressed in Mφ_APOE (Fig. 6G).