RT Journal Article
SR Electronic
T1 Novel insight into the aetiology of rheumatoid arthritis gained by a cross-tissue transcriptome-wide association study
JF RMD Open
JO RMD Open
FD EULAR
SP e002529
DO 10.1136/rmdopen-2022-002529
VO 8
IS 2
A1 Jing Ni
A1 Peng Wang
A1 Kang-Jia Yin
A1 Xiao-Ke Yang
A1 Han Cen
A1 Cong Sui
A1 Guo-Cui Wu
A1 Hai-Feng Pan
YR 2022
UL http://rmdopen.bmj.com/content/8/2/e002529.abstract
AB Background Although genome-wide association studies (GWASs) have identified more than 100 loci associated with rheumatoid arthritis (RA) susceptibility, the causal genes and biological mechanisms remain largely unknown.Methods A cross-tissue transcriptome-wide association study (TWAS) using the unified test for molecular signaturestool was performed to integrate GWAS summary statistics from 58 284 individuals (14 361 RA cases and 43 923 controls) with gene-expression matrix in the Genotype-Tissue Expression project. Subsequently, a single tissue by using FUSION software was conducted to validate the significant associations. We also compared the TWAS with different gene-based methodologies, including Summary Data Based Mendelian Randomization (SMR) and Multimarker Analysis of Genomic Annotation (MAGMA). Further in silico analyses (conditional and joint analysis, differential expression analysis and gene-set enrichment analysis) were used to deepen our understanding of genetic architecture and comorbidity aetiology of RA.Results We identified a total of 47 significant candidate genes for RA in both cross-tissue and single-tissue test after multiple testing correction, of which 40 TWAS-identified genes were verified by SMR or MAGMA. Among them, 13 genes were situated outside of previously reported significant loci by RA GWAS. Both TWAS-based and MAGMA-based enrichment analyses illustrated the shared genetic determinants among autoimmune thyroid disease, asthma, type I diabetes mellitus and RA.Conclusion Our study unveils 13 new candidate genes whose predicted expression is associated with risk of RA, providing new insights into the underlying genetic architecture of RA.The datasets used and/or analysed during the current study are available from the corresponding authors on reasonable request.