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.

backgroundPrevious 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.

methodWe performed two-sample Mendelian randomisation (2SMR) using spatial expression quantitative trait loci (eQTLs) from nine tissue-specific gene-regulatory networks as instrumental variables (IVs). We also identified JIA-associated SNPs from previous GWAS and mapped their spatial eQTL effects across 49 human tissues. These SNP sets were then used as features in a Lasso-regularised logistic regression model to predict JIA disease status. The model weight magnitudes served as proxies for each SNP's contribution to JIA risk. We evaluated the robustness of our model's feature ranking across 50 cross-validation runs.

resultsThe top-ranked SNPs included rs7775055, which tags the human leukocyte antigen (HLA) class II haplotype

conclusionBy applying a machine learning approach to rank SNP-gene-tissue contributions to JIA risk, our findings offer insights into the genetic mechanisms underlying JIA pathogenesis. Future experimental validation could facilitate new therapeutic targets for the treatment or prevention of JIA.