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. 2025 Oct 22;19(1):120.
doi: 10.1186/s40246-025-00824-8.

Association between hot spring residency and dry eye disease: a crossover gene-environment interaction (GxE) study in Taiwan

Hsin-Yu Wu #  1   2 , Kao-Jung Chang #  1   3   4   5 , Wei Chiu #  1   2 , Ching-Yun Wang #  6 , Yi-Chen Lin  7 , Yu-Tien Hsu  8 , Yuan-Chih Wen  2   9   10 , Pin-Hsuan Chiang  1   7   11 , Yu-Hsiang Chen  1   2 , He-Jhen Dai  1   2 , Chia-Hsin Lu  1   2 , Yi-Cheng Chen  1   2 , Han-Ying Tsai  1   7   11 , Yu-Chun Chen  2   12   13 , Chih-Hung Hsu  14   15 , Shih-Hwa Chiou  1   4   16 , Yi-Ping Yang  1   17 , Ai-Ru Hsieh #  18 , Chih-Chien Hsu  19   20   21
Affiliations

Association between hot spring residency and dry eye disease: a crossover gene-environment interaction (GxE) study in Taiwan

Hsin-Yu Wu et al. Hum Genomics. .

Abstract

Background/aims: The advent of genetic biobanking has powered gene-environment interaction (GxE) studies in various disease contexts. Therefore, we aimed to discover novel GxE effects that address hot spring residency as a risk to inconspicuous disease association.

Methods: A complete genetic and demographic registry comprising 129,451 individuals was obtained from Taiwan Biobank (TWB). After geographical disease prevalence was analyzed to identify putative disease association with hot-spring residency, multivariable regression and logistic regression were rechecked to exclude socioeconomic confounders in geographical-disease association. Genome-wide association study (GWAS), gene ontology (GO), and protein-protein interaction (PPI) analysis identified predisposing genetic factors among hotspring-associated diseases. Lastly, a polygenic risk score (PRS) model was formulated to stratify environmental susceptibility in accord with their genetic predisposition.

Results: After socioeconomic covariate adjustment, prevalence of dry eye disease (DED) was significantly associated with hot spring distribution. Through single nucleotide polymorphisms (SNPs) discovery and subsequent PPI pathway aggregation, CDKL2 kinase pathways were significantly enriched in hot-spring specific DED functional SNPs. Notably, PRS predicted disease well in hot spring regions (AUC = 0.9168). Hot spring and discovered SNPs contributed to crossover GxE effect on DED (relative risk (RR)G+E-= 0.99; RRG-E+ = 0.35; RRG+E+ = 2.04).

Conclusion: We identified hot-spring exposure as a modifiable risk in the PRS-predicted GxE context of DED.

Keywords: Dry eye disease; Gene–environment association; Genome-wide association study; Hot spring; Polygenic risk score; Taiwan biobank.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Ethical approvals were obtained from the Institutional Review Board (IRB) of the Taipei Veterans General Hospital with reference numbers 2023-01-006AC. All participants provided written informed consent. This study was conducted in compliance with the Helsinki Declaration. Patients or the public WERE NOT involved in the design, or conduct, or reporting, or dissemination plans of our research. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Discovering the environmental (E) effect on DED prevalence. A Association of hot spring subtype and ocular disease (DED and floaters). B The geospatial information system (GIS) map of hot spring-related ocular diseases. Each dot indicates a hot spring outcrop. C Logistic regression models evaluating socioeconomic (S) confounding effects. The dashed line indicated odds ratio (OR) = 1.0. D Logistic regression models evaluating other environmental confounding effects. The dashed line indicated odds ratio (OR) = 1.0. DED, dry eye disease
Fig. 2
Fig. 2
Manhattan plots of genome-wide association studies (GWASs) among “All”, “Non-hot Spring”, and “Hot Spring” gene sets. Blue lines represented p-value = 10− 4 while red lines represented p-value = 10− 5
Fig. 3
Fig. 3
Gene ontology. A Screening single nucleotide polymorphisms (SNPs) based on threshold of p-value < 0.005. AUC, Area Under Curve. B Protein‒protein interaction (PPI) network. C Comparing signaling pathways of co-expressed genes between gene set from GA and Gh. The dashed line indicated p-value < 0.05. D Venn diagram of functional SNP genes from all, non-hot spring, and hot spring population. GA: all population gene sets; GN: non-hot spring population gene sets; GH: hot spring population gene sets; and Gh: GH that did not overlap with GA and GN. E Activated pathways of functional genes within PPI network of hot spring-specific gene set (Gh) based on Reactome 2022
Fig. 4
Fig. 4
Polygenic risk score (PRS) models evaluating gene-environment interactions (GxE). A Density distribution among PRS score. After adjusted for linkage disequilibrium (LD) clumping threshold (r2 < 0.01) and genome-wide significance level threshold (p-value < 0.005), variant-disease association (VDA) results were depicted as GA, GN, and GH for “all”, “non hot spring”, and “hot spring” gene sets. B Percentage of case and control in each PRS quartile. C Odds ratio (OR) of each PRS quartile. The dashed line indicated OR = 1.0. D Area Under Curve (AUC) of different combinations of gene set (G) and environmental condition (E). E Minor allele frequency (MAF) between hot spring and non-hot spring regions
Fig. 5
Fig. 5
Net benefit of identifying the risk of gene-environment interactions (GxE). A Density profile of GH polygenic risk score (PRS). B Multivariate logistic regression evaluating the GxE effect. C Relative risk (RR) with 95% confidence interval (CI) between different environmental conditions and GHPRS. D Nomogram results estimating predictability of GxE. E Decision curve analysis (DCA) assessing net benefit of identifying GxE risk. PRS, polygenic risk score; Mo, mode; Md, median; GH, gene set from “hot spring”; and DED, dry eye disease
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