Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis

Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (P(FUSION) = 5.12 × 10(−6), P(eMAGMA) = 1.94 × 10(−7) for whole blood), IL-6R (P(FUSION) = 8.63 × 10(−7), P(eMAGMA) = 2.74 × 10(−6) for cells EBV-transformed lymphocytes), and Fas (P(FUSION) = 5.21 × 10(−5), P(eMAGMA) = 1.08 × 10(−6) for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (P(FUSION) = 2.10 × 10(−7), P(eMAGMA) = 3.93 × 10(−8) for Liver), LAT (P(FUSION) = 1.53 × 10(−4), P(eMAGMA) = 4.62 × 10(−7) for Artery Aorta), and MAGI3 (P(FUSION) = 1.30 × 10(−5), P(eMAGMA) = 1.73 × 10(−7) for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA.