ITMO University, 197101 Saint Petersburg, Russia.
Bekhterev National Medical Research Center, 192019 Saint Petersburg, Russia.
HGG Adv. 2023 May 5;4(3):100203. doi: 10.1016/j.xhgg.2023.100203. eCollection 2023 Jul 13.
We introduce a user-friendly tool for risk gene, cell type, and drug prioritization for complex traits: GCDPipe. It uses gene-level GWAS-derived data and gene expression data to train a model for the identification of disease risk genes and relevant cell types. Gene prioritization information is then coupled with known drug target data to search for applicable drug agents based on their estimated functional effects on the identified risk genes. We illustrate the utility of our approach in different settings: identification of the cell types, implicated in disease pathogenesis, was tested in inflammatory bowel disease (IBD) and Alzheimer disease (AD); gene target and drug prioritization was tested in IBD and schizophrenia. The analysis of phenotypes with known disease-affected cell types and/or existing drug candidates shows that GCDPipe is an effective tool to unify genetic risk factors with cellular context and known drug targets. Next, analysis of the AD data with GCDPipe suggested that gene targets of diuretics, as an Anatomical Therapeutic Chemical drug subgroup, are significantly enriched among the genes prioritized by GCDPipe, indicating their possible effect on the course of the disease.
我们引入了一个用户友好的工具，用于复杂性状的风险基因、细胞类型和药物优先级排序：GCDPipe。它使用基于基因水平 GWAS 的数据和基因表达数据来训练模型，以识别疾病风险基因和相关细胞类型。然后，将基因优先级排序信息与已知的药物靶标数据相结合，根据对鉴定出的风险基因的估计功能影响，搜索适用的药物制剂。我们在不同的环境中说明了我们方法的实用性：在炎症性肠病 (IBD) 和阿尔茨海默病 (AD) 中测试了疾病发病机制中涉及的细胞类型的鉴定；在 IBD 和精神分裂症中测试了基因靶标和药物优先级排序。对具有已知疾病相关细胞类型和/或现有药物候选物的表型的分析表明，GCDPipe 是将遗传风险因素与细胞环境和已知药物靶标统一起来的有效工具。接下来，使用 GCDPipe 对 AD 数据的分析表明，利尿剂的药物靶标（作为一个解剖治疗化学药物亚组）在 GCDPipe 优先排序的基因中显著富集，表明它们可能对疾病的进程有影响。
