Wang Yu, Chen Jiahao, Yao Hang, Li Yuxin, Xu Xiaogang, Zhang Delin
Graduate School of Jiangxi University of Traditional Chinese Medicine, Nanchang, China.
School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.
Front Genet. 2024 Aug 5;15:1426860. doi: 10.3389/fgene.2024.1426860. eCollection 2024.
This study aims to prioritize genes potentially involved in multifactorial or causal relationships with gout.
Using the Summary Data-based Mendelian Randomization (SMR) approach, this research analyzed expression quantitative trait loci (eQTL) data from blood and renal tissues and genome-wide association study (GWAS) data related to gout. It sought to identify genetic loci potentially involved in gout. Heterogeneity testing was conducted with the HEIDI test, and results were adjusted for the False Discovery Rate (FDR). Blood cis-eQTL data were sourced from the eQTLGen Consortium's summary-level data, and renal tissue data came from the V8 release of the GTEx eQTL summary data. Gout GWAS data was sourced from the FinnGen Documentation of the R10 release.
SMR analysis identified 14 gene probes in the eQTLGen blood summary-level data significantly associated with gout. The top five ranked genes are: ENSG00000169231 (labeled THBS3, P = 4.16 × 10), ENSG00000231064 (labeled THBS3-AS1, P = 1.88 × 10), ENSG00000163463 (labeled KRTCAP2, P = 3.88 × 10), ENSG00000172977 (labeled KAT5, P = 1.70 × 10), and ENSG00000161395 (labeled PGAP3, P = 3.24 × 10). Notably, increased expression of KRTCAP2 and PGAP3 is associated with an increased risk of gout, whereas increased expression of THBS3, THBS3-AS1, and KAT5 is associated with a reduced gout risk. No significant gene associations with gout were observed in renal tissue, likely due to the limited sample size of kidney tissue.
Our findings have highlighted several genes potentially involved in the pathogenesis of gout. These results offer valuable insights into the mechanisms of gout and identify potential therapeutic targets for its treatment.
本研究旨在对可能与痛风存在多因素或因果关系的基因进行优先级排序。
本研究采用基于汇总数据的孟德尔随机化（SMR）方法，分析了血液和肾脏组织的表达定量性状位点（eQTL）数据以及与痛风相关的全基因组关联研究（GWAS）数据。旨在识别可能与痛风相关的基因位点。使用HEIDI检验进行异质性检验，并对结果进行错误发现率（FDR）校正。血液顺式eQTL数据来自eQTLGen联盟的汇总水平数据，肾脏组织数据来自GTEx eQTL汇总数据的V8版本。痛风GWAS数据来自FinnGen文档的R10版本。
SMR分析在eQTLGen血液汇总水平数据中鉴定出14个与痛风显著相关的基因探针。排名前五的基因是：ENSG00000169231（标记为THBS3，P = 4.16×10）、ENSG00000231064（标记为THBS3-AS1，P = 1.88×10）、ENSG00000163463（标记为KRTCAP2，P = 3.88×10）、ENSG00000172977（标记为KAT5，P = 1.70×10）和ENSG00000161395（标记为PGAP3，P = 3.24×10）。值得注意的是，KRTCAP2和PGAP3表达增加与痛风风险增加相关，而THBS3、THBS3-AS1和KAT5表达增加与痛风风险降低相关。在肾脏组织中未观察到与痛风显著相关的基因，可能是由于肾脏组织样本量有限。
我们的研究结果突出了几个可能参与痛风发病机制的基因。这些结果为痛风的发病机制提供了有价值的见解，并确定了潜在的治疗靶点。
