ArticleFrontiers in genetics2024
Integrative analysis reveals novel insights into juvenile idiopathic arthritis pathogenesis and shared molecular pathways with associated traits.
Article in Frontiers in genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- eQTL analysis: A bridge from genome to mechanism.Genes & diseases · 2026Review
- Article
- From arthritis to central sensitization: targeting the neuro-immune axis via microglial modulation in acupuncture treatment for Juvenile Idiopathic Arthritis-associated pain.Frontiers in immunology · 2025Review
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Authors and funding
3 authors.
Funding
Abstract
Background: Juvenile idiopathic arthritis (JIA) is an autoimmune joint disease that frequently co-occurs with other complex phenotypes, including cancers and other autoimmune diseases. Despite the identification of numerous risk variants through genome-wide association studies (GWAS), the affected genes, their connection to JIA pathogenesis, and their role in the development of associated traits remain unclear. This study aims to address these gaps by elucidating the gene-regulatory mechanisms underlying JIA pathogenesis and exploring its potential role in the emergence of associated traits. Methods: A two-sample Mendelian Randomization (MR) analysis was conducted to identify blood-expressed genes causally linked to JIA. A curated protein interaction network was subsequently used to identify sets of single-nucleotide polymorphisms (i.e., spatial eQTL SNPs) that regulate the expression of JIA causal genes and their protein interaction partners. These SNPs were cross-referenced against the GWAS catalog to identify statistically enriched traits associated with JIA. Results: The two-sample MR analysis identified 52 genes whose expression changes in the blood are putatively causal for JIA. These genes (e.g., Conclusion: Our approach provides a significant advance in understanding the genetic architecture of JIA and associated traits. The results suggest that the burden of associated traits may differ among JIA patients, influenced by their combined genetic risk across different clusters of traits. Future experimental validation of the identified connections could pave the way for refined patient stratification, the discovery of new biomarkers, and shared therapeutic targets.
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