| Title: | Genetic variants associated with gout identified through a genome-wide study in the UK biobank (N = 150 542) |
| Journal: | Human Molecular Genetics |
| Published: | 11 Oct 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41075270/ |
| DOI: | https://doi.org/10.1093/hmg/ddaf151 |
Publication 19210
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Abstract
Gout is a prevalent and painful form of inflammatory arthritis associated with hyperuricemia, which leads to monosodium urate crystal deposition in joints and surrounding tissues, triggering acute inflammatory responses. This disease is also closely linked to serious comorbidities, including cardiovascular diseases, chronic kidney diseases, diabetes, and increased mortality risk, significantly impacting global health. In this study, we conducted a comprehensive genome-wide association study (GWAS) based on the UK Biobank pain questionnaire 2019, comprising 10 474 gout cases and 140 068 controls, identifying 13 loci associated with gout. These findings were further explored in the FinnGen cohort, with 10 loci being replicated significantly. Sex-stratified analyses revealed notable differences, with 16 loci identified in males and two loci identified in females, reflecting both shared and sex -stratified genetic influences on gout susceptibility. In addition, genetic correlation analyses demonstrated strong associations between gout and traits related to urate levels, specific medication use, and metabolic functions. Transcriptome-wide association studies highlighted several genes, such as SLC16A9 and ASAH2B, which showed significant expression patterns across various tissues, implicating metabolic and immune pathways in gout. Phenome-wide association studies of significant single nucleotide polymorphisms revealed links to metabolic, immunological, and skeletal traits, underscoring the multi-faceted nature of gout. These results contribute valuable insights into the genetic architecture and biological mechanisms underlying gout, suggesting potential avenues for tailored interventions.</p>
9 Authors
- Yiwen Tao
- Tengda Cai
- Qi Pan
- Luning Yang
- Sen Lin
- Mainul Haque
- Tania Dottorini
- Abhishek Abhishek
- Weihua Meng
1 Application
| Application ID | Title |
|---|---|
| 89386 | To explore the genetic mechanisms of multiple pain phenotypes based on the UK Biobank cohort |
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