孟德尔随机化
医学
类风湿性关节炎
药品
药物遗传学
药物基因组学
样本量测定
临床试验
生物信息学
计算生物学
基因
内科学
药理学
生物
遗传学
基因型
遗传变异
统计
数学
作者
Yu Cao,Ying Yang,Qingfeng Hu,Guojun Wei
标识
DOI:10.1186/s12967-023-04474-z
摘要
Abstract Introduction Rheumatoid arthritis (RA) is a chronic inflammatory illness that mostly affects the joints of the hands and feet and can reduce life expectancy by an average of 3 to 10 years. Although tremendous progress has been achieved in the treatment of RA, a large minority of patients continue to respond poorly to existing medications, owing in part to a lack of appropriate therapeutic targets. Methods To find therapeutic targets for RA, a Mendelian randomization (MR) was performed. Cis-expression quantitative trait loci (cis-eQTL, exposure) data were obtained from the eQTLGen Consortium (sample size 31,684). Summary statistics for RA (outcome) were obtained from two largest independent cohorts: sample sizes of 97,173 (22,350 cases and 74,823 controls) and 269,377 (8279 cases and 261,098), respectively. Colocalisation analysis was used to test whether RA risk and gene expression were driven by common SNPs. Drug prediction and molecular docking was further used to validate the medicinal value of drug targets. Results Seven drug targets were significant in both cohorts in MR analysis and supported by localization. PheWAS at the gene level showed only ATP2A1 associated with other traits. These genes are strongly associated with immune function in terms of biological significance. Molecular docking showed excellent binding for drugs and proteins with available structural data. Conclusion This study identifies seven potential drug targets for RA. Drugs designed to target these genes have a higher chance of success in clinical trials and is expected to help prioritise RA drug development and save on drug development costs.
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