Background: Osteoarthritis (OA) is a common clinical disease caused by a variety of factors, including genetic variants. Although genome-wide association studies (GWAS) have been performed to elucidate the genetic basis of OA, some loci of risk located in noncoding regions of the genome have been neglected.Therefore, we integrated multiple data types to detect the genetic component of gene expression in OA patients through transcriptome-wide association studies (TWAS) and summary-data-based Mendelian randomization (SMR) analysis.Methods: TWAS was performed by integrating the larger GWAS summary-data for OA (n=30,727 cases, n=297,191 controls) and 2 expression weight sets (muscle-skeletal tissue and whole blood). Colocalization analysis, conditional analysis, and fine-mapping analysis were also conducted. A broad description of the identified associations was obtained. In addition, a causal relationship between certain risk genes and OA was identified with SMR.Results: New significant genome-wide associations were found, including on chromosome 1q36.12 (rs1555024, P=4.24E−07) near the ASAP3 and TCEA3 genes, on chromosome 17q24.2 (rs2521348, P=1.01E−06) near the ABCA9 gene, on chromosome 20q11.22 (rs224331, P=8.17E−09) near the UQCC1 and MYH7B genes, and on chromosome 21q21.3 (rs2832155, P=5.39E−08) near the RWDD2B gene. In addition, SMR results exhibited that upregulated UQCC1 and downregulated ASAP3 were associated with OA development and both had a significant causal relationship with OA. Conclusions:We revealed some novel OA-associated genes and risk loci by integrating multiple data types and analysis methods, thus providing new clues for the study of genetic mechanisms of OA.

Background Through the bioinformatics analysis to screen out the potential chromatin regulators (CRs) under the immune infiltration of osteoarthritis (OA), thus providing some theoretical support for future studies of epigenetic mechanisms under OA immune infiltration. Methods By integrating CRs and the OA gene expression matrix, we performed weighted gene co-expression network analysis (WGCNA), differential analysis, and further screened Hub genes by protein-protein interaction (PPI) analysis. Using the OA gene expression matrix, immune infiltration extraction and quantification were performed to analyze the correlations and differences between immune infiltrating cells and their functions. By virtue of these Hub genes, Hub gene association analysis was completed and their upstream miRNAs were predicted by the FunRich software. Moreover, a risk model was established to analyze the risk probability of associated CRs in OA, and the confidence of the results was validated by the validation dataset. Results This research acquired a total of 32 overlapping genes, and 10 Hub genes were further identified. The strongest positive correlation between dendritic cells and mast cells and the strongest negative correlation between parainflammation and Type I IFN reponse. In the OA group DCs, iDCs, macrophages, MCs, APC co-inhibition, and CCR were significantly increased, whereas B cells, NK cells, Th2 cells, TIL, and T cell co-stimulation were significantly decreased. The risk model results revealed that BRD1 might be an independent risk factor for OA, and the validation dataset results are consistent with it. 60 upstream miRNAs of OA-related CRs were predicted by the FunRich software. Conclusion This study identified certain potential CRs and miRNAs that could regulate OA immunity, thus providing certain theoretical supports for future epigenetic mechanism studies on the immune infiltration of OA.