Genome-wide association study of dry eye disease reveals shared heritability with systemic comorbidities

Abstract

Dry eye disease (DED) affects up to 25% of the adult population, with chronic symptoms of pain and dryness often negatively impacting quality of life. The genetic architecture of DED is largely unknown. Here, we develop and validate an algorithm for DED in the Million Veteran Program using a combination of diagnosis codes and prescription records, resulting in 132,657 cases and 352,201 controls. In a multi-ancestry genome-wide association study, we identify ten significant loci in nine susceptibility regions with largely consistent effects across ancestries, including loci linked to synapse maintenance (EPHA5, GRIA1, SYNGAP1) and autoimmunity (BLK). Phenome-wide scans for genetic pleiotropy indicate substantial genetic correlations of DED with comorbidities, including fibromyalgia, post-traumatic stress disorder, and Sjögren's disease. Finally, applying genomic structural equation modeling, we derive a latent factor underlying DED and other chronic pain traits which accounts for 51% of the genetic variance of DED.

Keywords: comorbidities; dry eye disease; genome-wide association study; heritability.

Figure 1.. Overall study design.

We developed and validated a dry eye disease algorithm based on a combination of diagnosis codes and prescription records and applied it to the Million Veteran Program (MVP) cohort. We inferred the genetic ancestry of participants; four ancestries (EUR, AFR, AMR, and EAS) had sufficient sample size for stratified analytic sets. Polygenic score association scans and GWAS were performed in each ancestry, and GWAS summary statistics were combined in a multi-ancestry meta-analysis. Replication of the 10 genome-wide significant loci was performed in the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. The GERA sample size shown corresponds to the GERA broad (ICD code-based) phenotype definition. Heritability, genetic correlation, and genomic structural equation modeling analyses were further performed on the EUR GWAS summary statistics. VAMC: Veterans Affairs Medical Center; EUR: European ancestry; AFR: African ancestry; AMR: American (Hispanic/Latino) ancestry; EAS: East Asian ancestry; PGS Catalog: Polygenic Score Catalog; GWAS: genome-wide association study.

Figure 2.. Phenome-wide scan of association between disease-related polygenic scores and dry eye disease (DED) case-control status.

Each polygenic score (PGS) in the PGS Catalog was tested for association with DED. Results for European-ancestry participants (87,444 DED cases and 258,228 DED controls) are shown. Full summary statistics are provided in Table S4. ADHD: attention deficit hyperactivity disorder; COPD: chronic obstructive pulmonary disorder; GERD: gastro-esophageal reflux disease; T2D: type 2 diabetes.

Figure 3.. Manhattan plot of GWAS results for dry eye disease.

The multi-ancestry meta-analysis of four ancestries represented in the Million Veteran Program is shown (total of 132,637 cases and 352,201 controls). X-axis, chromosome position; Y-axis, −log10 (p-value). The red line denotes the genome-wide significance threshold (P < 5×10⁻⁸). Genome-wide significant loci at each of the nine novel susceptibility regions are highlighted and labeled with the nearest gene.

Figure 4.. Genetic correlations of selected traits with dry eye disease.

Star represents P < 0.001 (un-adjusted). GERD: gastro-esophageal reflux disease; COPD: chronic obstructive pulmonary disorder; PTSD: post-traumatic stress disorder; ADHD: attention deficit hyperactivity disorder; HDL cholesterol: high-density lipoprotein cholesterol; 95% c.i.: 95% confidence interval.

Figure 5.. Genomic structural equation modeling of DED identifies shared pain risk.

a) Heatmap of pairwise genetic correlations between DED and representative chronic pain traits. The SNP heritability (SNP-h²) is represented along the diagonal. b) The genomic SEM model used to derive a latent factor representing the shared genetic risk underlying DED and other chronic pain traits. The arrows pointing from the shared latent factor to each trait represent the factor loading, and the arrows pointing from u to each trait represent the residual variance. Model fit indices are provided below the model diagram. c) Forest plot of genetic correlations of the latent factor with DED-linked traits of interest. IBS: irritable bowel syndrome; MCP: multisite chronic pain; SEM: structural equation modeling; u: residual; df: degrees of freedom; AIC: akaike information criterion; CFI: comparative fit index; SRMR: standardized root mean square residual; r_G: genetic correlation; 95% CI: 95% confidence interval; BMI: body mass index; COPD: chronic obstructive pulmonary disorder; GERD: gastro-esophageal reflux disease; ADHD: attention deficit hyperactivity disorder; PTSD: post-traumatic stress disorder.