{Reference Type}: Journal Article
{Title}: Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis.
{Author}: Liu S;Gong W;Liu L;Yan R;Wang S;Yuan Z;
{Journal}: Int J Mol Sci
{Volume}: 23
{Issue}: 21
{Year}: Nov 2022 4
{Factor}: 6.208
{DOI}: 10.3390/ijms232113555
{Abstract}: 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 (PFUSION = 5.12 × 10-6, PeMAGMA = 1.94 × 10-7 for whole blood), IL-6R (PFUSION = 8.63 × 10-7, PeMAGMA = 2.74 × 10-6 for cells EBV-transformed lymphocytes), and Fas (PFUSION = 5.21 × 10-5, PeMAGMA = 1.08 × 10-6 for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (PFUSION = 2.10 × 10-7, PeMAGMA = 3.93 × 10-8 for Liver), LAT (PFUSION = 1.53 × 10-4, PeMAGMA = 4.62 × 10-7 for Artery Aorta), and MAGI3 (PFUSION = 1.30 × 10-5, PeMAGMA = 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.