Multi-omics profiling uncovers candidate genes for the treatment of graves’ disease

Dec 22, 2025Immunopharmacology and immunotoxicology

Using multiple biological analyses to find potential treatment genes for Graves' disease

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Circadian Biology on OpenScience ↗PubMed ↗DOI ↗

Abstract

Graves' disease is associated with 1,772 methylation sites linked to 294 genes.

Methylation analysis identified significant associations between Graves' disease and numerous genetic loci.
Gene expression analysis revealed 157 genes associated with Graves' disease.
Protein quantitative trait loci analysis identified signals in two independent cohorts, with 20 genes implicated.
AGER and AIF1 consistently showed strong associations with Graves' disease across multiple omics analyses.
Integrated analysis indicated a positive correlation between AGER DNA methylation and its expression, while AIF1 exhibited an inverse relationship.
Drug predictions suggest potential therapeutic agents for AGER and AIF1 that may influence Graves' disease.

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BACKGROUND: This Summary-data-based Mendelian randomization (SMR) study investigates the genetic basis of Graves' disease (GD) using a multi-omics approach to elucidate biological mechanisms and identify therapeutic candidates.

METHODS: SMR and Bayesian colocalization analyzed GD associations with methylation (mQTL), expression (eQTL), and protein quantitative trait loci (pQTL) using FinnGen as the discovery cohort. Top SNPs underwent methylation-gene expression and gene expression-protein interaction analyses. Key findings were validated against an independent GWAS dataset. Functional enrichment characterized pathways for eQTL-SMR-derived loci. The Drug Signatures Database (DsigDB) predicted potential drugs.

RESULTS: Significant multi-omics associations were found. mQTL analysis linked GD to 1,772 methylation sites (294 genes); eQTL identified 157 genes. pQTL signals appeared in two cohorts (4 genes deCODE; 16 genes UKB-PPP). AGER and AIF1 were consistent across omics. AIF1 showed strong GD associations (mQTL OR 1.09; eQTL OR 1.44; deCODE-pQTL OR 3.17; UKB-PPP-pQTL OR 3.46). Integrated analysis revealed a positive correlation between AGER DNA methylation and expression, but negative for AIF1, with colocalization support (PPH3 + PPH4 > 0.8). SMR showed inverse pQTL associations for AGER and positive for AIF1, also colocalized. Independent GWAS validation confirmed GD associations with AGER and AIF1 at pQTL. Enrichment indicated immune/inflammatory pathways. Drug predictions included agents for AGER (Oleanolic Acid, Rutin, Estriol, GW9662) and AIF1 (Quinoline).

CONCLUSION: This study provides novel insights into GD pathogenesis, highlighting AGER and AIF1 as therapeutically relevant genes. Findings offer valuable insights for GD treatment strategies and potential drug development streamlining, warranting further experimental validation.

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Multi-omics profiling uncovers candidate genes for the treatment of graves’ disease

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