Machine learning using genotype and gene-expression data identifies alterations of genes involved in infection susceptibility, antigen presentation and cytokine signalling as key contributors to JIA risk prediction.
Journal:
RMD open
Published Date:
Jul 9, 2025
Abstract
BACKGROUND: Previous genome-wide association studies (GWAS) have identified numerous genetic loci associated with juvenile idiopathic arthritis (JIA). However, the functional impact of these variants-particularly on tissue-specific gene expression-and which regulatory interactions make the greatest relative contribution to JIA risk remain unclear. Identifying these key single-nucleotide polymorphism (SNP)-gene-tissue combinations can help prioritise targets for future functional studies and therapeutic interventions.
