Department of Epidemiology, School of Public Health, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
BMC Genomics. 2022 May 11;23(Suppl 4):362. doi: 10.1186/s12864-022-08580-y.
Multiple sclerosis (MS) is a debilitating immune-mediated disease of the central nervous system that affects over 2 million people worldwide, resulting in a heavy burden to families and entire communities. Understanding the genetic basis underlying MS could help decipher the pathogenesis and shed light on MS treatment. We refined a recently developed Bayesian framework, Integrative Risk Gene Selector (iRIGS), to prioritize risk genes associated with MS by integrating the summary statistics from the largest GWAS to date (n = 115,803), various genomic features, and gene-gene closeness.
We identified 163 MS-associated prioritized risk genes (MS-PRGenes) through the Bayesian framework. We replicated 35 MS-PRGenes through two-sample Mendelian randomization (2SMR) approach by integrating data from GWAS and Genotype-Tissue Expression (GTEx) expression quantitative trait loci (eQTL) of 19 tissues. We demonstrated that MS-PRGenes had more substantial deleterious effects and disease risk. Moreover, single-cell enrichment analysis indicated MS-PRGenes were more enriched in activated macrophages and microglia macrophages than non-activated ones in control samples. Biological and drug enrichment analyses highlighted inflammatory signaling pathways.
In summary, we predicted and validated a high-confidence MS risk gene set from diverse genomic, epigenomic, eQTL, single-cell, and drug data. The MS-PRGenes could further serve as a benchmark of MS GWAS risk genes for future validation or genetic studies.
多发性硬化症（MS）是一种使人衰弱的中枢神经系统免疫介导疾病，影响着全球超过 200 万人，给家庭和整个社区带来了沉重的负担。了解 MS 背后的遗传基础有助于解析发病机制并为 MS 治疗提供启示。我们改进了最近开发的贝叶斯框架——综合风险基因选择器（iRIGS），通过整合迄今为止最大的 GWAS 的汇总统计数据（n=115803）、各种基因组特征和基因-基因接近度，优先选择与 MS 相关的风险基因。
我们通过贝叶斯框架确定了 163 个与 MS 相关的优先风险基因（MS-PRGenes）。我们通过整合来自 GWAS 和 19 种组织的基因型-组织表达（GTEx）表达数量性状基因座（eQTL）数据的两样本孟德尔随机化（2SMR）方法，通过两样本孟德尔随机化（2SMR）方法复制了 35 个 MS-PRGenes。我们证明了 MS-PRGenes 具有更显著的有害影响和疾病风险。此外，单细胞富集分析表明，与对照样本中的非激活巨噬细胞和小胶质细胞相比，MS-PRGenes 在激活的巨噬细胞和小胶质细胞中更为丰富。生物和药物富集分析强调了炎症信号通路。
总之，我们从不同的基因组、表观基因组、eQTL、单细胞和药物数据中预测和验证了一个高度置信的 MS 风险基因集。MS-PRGenes 可以进一步作为未来验证或遗传研究的 MS GWAS 风险基因的基准。
