Overview of the study design
First, we performed SMR analysis to detect putative causal genes for RA by integrating data from RA GWAS with data from each of four eQTL studies. Second, we tested the probability of the identified genes clustering into a specific biological process by functional annotation enrichment analysis. Third, we evaluated the associations between mRNA levels of the identified genes and RA in blood cells and Synovial tissue. Fourth, the connections between novel genes and known RA genes were evaluated
Inference of RA causal genes
The SMR analysis identified a long list of genes as potential causal genes underlying RA GWAS associations (PSMR<5×10–6) (Supplementary Tables 1–3). Among these genes, 119 and 53 (140 unique) were found to be associated with RA in Europeans (Supplementary Table 2) and Asians (Supplementary Table 3), respectively. Of these genes, 52 passed the HEIDI tests (PHEIDI>0.05), suggesting that there was no heterogeneity, and the expression of these genes and RA were affected by same variants.
The current study represented an effort to identify RA-associated genes by integrating GWAS data with eQTL data using the SMR method. We identified a list of potential causal genes with high confidence for RA. The identified genes were highly connected and enriched in specific biological pathways which were related to RA. Moreover, the findings have highlighted a potential functional role of IFNAR2 in RA pathology.
Previous GWASs have confirmed hundreds of genetic variants for RA susceptibility.
CRediT authorship contribution statement
Xing-Bo Mo: Conceptualization, Methodology, Software, Data curation, Writing - original draft, Visualization, Investigation. Yang-Hua Sun: . Yong-Hong Zhang: Writing - review & editing. Shu-Feng Lei: Supervision, Writing - review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
The study was supported by Natural Science Foundation of China (81872681, 31401079 and 81473046), the Startup Fund from Soochow University (Q413900313), Natural Science Fund for Colleges and Universities in Jiangsu Province (18KJB330005), and a Project of the Priority Academic Program Development of Jiangsu Higher Education Institutions.
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