Abstract

Background

   Juvenile idiopathic arthritis (JIA) is the most common rheumatic
   disease in children, and its pathogenesis is still unclear. Genome-wide
   association studies (GWASs) of JIA have identified hundreds of risk
   factors, but few of them implicated specific biological mechanisms.

Methods

   A cross-tissue transcriptome-wide association study (TWAS) was
   performed with the functional summary-based imputation software
   (FUSION) tool based on GWAS summary datasets (898 JIA patients and
   346,102 controls from BioBank Japan (BBJ)/FinnGen). The gene expression
   reference weights of skeletal muscle and the whole blood were obtained
   from the Genotype-Tissue Expression (GTExv8) project. JIA-related genes
   identified by TWAS findings genes were further compared with the
   differentially expressed genes (DEGs) identified by the mRNA expression
   profile of JIA from the Gene Expression Omnibus (GEO) database
   (accession number: [35]GSE1402). Last, candidate genes were analyzed
   using functional enrichment and annotation analysis by Metascape to
   examine JIA-related gene sets.

Results

   The TWAS identified 535 significant genes with P < 0.05 and contains
   350 for Asian and 195 for European (including 10 genes both expressed
   in Asian and European), such as CDC16 (P = 1.72E-03) and PSMD5-AS1 (P =
   3.65E-02). Eight overlapping genes were identified based on TWAS
   results and DEGs of JIA patients, such as SIRPB1 (P [TWAS] = 4.21E-03,
   P [DEG] = 1.50E-04) and FRAT2 (P [TWAS] = 2.82E-02, P [DEG] =
   1.43E-02). Pathway enrichment analysis of TWAS identified 183 pathways
   such as cytokine signaling in the immune system and cell adhesion
   molecules. By integrating the results of DEGs pathway and process
   enrichment analyses, 19 terms were identified such as positive
   regulation of T-cell activation.

Conclusion

   By conducting two populations TWAS, we identified a group of
   JIA-associated genes and pathways, which may provide novel clues to
   uncover the pathogenesis of JIA.

   Keywords: transcriptome-wide association study, juvenile idiopathic
   arthritis, mRNA expression profiles, gene ontology, pathway enrichment

Introduction

   Juvenile idiopathic arthritis (JIA) is a group of arthritis of unknown
   origin that begins before the age of 16 and persists for more than 6
   weeks ([36]1). JIA is the most common childhood chronic rheumatic
   disease, has a prevalence of 3.8-400 cases per 100,000 in high-income
   countries, and causes damage to physical development, psychiatric
   development, and disabilities in children ([37]2). The high prevalence
   and severe consequences of JIA bring enormous social and economic
   burdens to society, but there is still no clear underlying mechanism of
   JIA development.

   The pathogenesis of JIA remains unclear, but it is thought to be
   multifactorial with complex interactions between genetic susceptibility
   and environmental factors ([38]3). It has been shown that JIA is
   similar to other autoimmune diseases, with which it shares
   susceptibility genes, mainly in the human leukocyte antigen (HLA)
   region ([39]4–[40]6). In addition, evidence from twin and familial
   studies suggested a genetic predisposition for JIA, with a heredity of
   13% ([41]7, [42]8). In recent years, an increasing number of studies
   have focused on the genetic mechanism of JIA. A genome-wide linkage
   study of 121 JIA-affected sibling-pair families suggested that genes in
   the HLA influence the risk of JIA ([43]9). In the era of genome-wide
   association studies (GWASs), this novel approach has identified several
   JIA-associated loci and genes, such as VTCN1, 3q13 within C3orf1, 10q21
   near JMJD1C, 4q31 ([44]10, [45]11). However, most of the variants at
   loci are often located in non-coding region ([46]12).

   Gene expression is a key step linking DNA sequence variation to
   phenotypes, which limits the use of GWASs in evaluating the risk of
   disease. Therefore, the specific biological mechanisms need to be
   further investigated. A previous study showed that many genetic
   variants play vital roles in complex traits by modulating gene
   expression ([47]13). In 2018, a new omics analysis method called
   transcriptome-wide association studies (TWASs) emerged, which leverage
   expression reference panels (eQTL cohorts with expression and genotype
   data) to discover gene–trait associations in GWAS datasets, providing a
   powerful strategy that integrates GWASs and gene expression references