摘要
目的基于生物信息学方法识别重症疟疾(SM)风险基因,揭示SM遗传基础,协助深入了解其发病机制并为改进治疗措施提供科学依据。方法从疟疾基因组流行病学网络的全基因组关联研究(GWAS)荟萃分析项目中获取SM的GWAS汇总数据(8699例SM病例和8357例对照),从GTEx、CAGE数据库中下载表达数量性状位点(eQTL)数据。应用复杂疾病驱动组织检测框架(DESE)确定SM驱动组织和关联基因,基因本体(GO)富集分析揭示基因本体信息;应用基于汇总数据的孟德尔随机化(SMR)分析方法,整合GWAS数据与各组织e QTL数据,确定SM潜在因果基因;应用S-PrediXcan软件和基于GTEx eQTL数据集的预训练模型,对SM进行全转录组关联分析(TWAS)。结果DESE确定3种驱动组织和24个关联基因,其中血液是最重要驱动组织,GO分析提示关联基因主要涉及嗅觉转导和红细胞相关功能;SMR和TWAS分析共定位了3个潜在因果基因ABO、HBG1、LINC00886,分别在血液、肾上腺、神经等多组织中表达并影响SM风险。结论基于大规模GWAS和e QTL数据识别了新的SM风险基因HBG1和LINC00886,其有潜力作为风险识别和预防治疗的候选基因。
Objective This study aimed to identify risk genes for severe malaria(SM)based on bioinformatic methods,unravel the genetic basis of SM to look into its pathogenesis and provide scientific evidence for improving therapeutic measures.Methods The summary result of a SM genome-wide association studies(GWAS)(8699 SM cases and 8357controls)was obtained from the malaria genomic epidemiology network GWAS meta-analysis.The expression quantitative trait locus(eQTL)data was downloaded from genotype-tissue-expression(GTEx)and consortium for the architecture of gene expression(CAGE)databases.Causal tissue detection framework was used for complex diseases(DESE)to determine SM driver tissues and associated genes,and gene ontology(GO)enrichment analysis was utilized to reveal their gene ontology informations.GWAS signals were integrated for SM with e QTL data for various tissues to identify SM potential causal genes using the summary data based mendelian randomization(SMR)analysis.S-PrediXcan software and pre-trained prediction models from GTEx eQTL database were applied to conduct the transcriptome-wide association study(TWAS)on SM.Results DESE determined three potential driver tissues and 24 associated genes among which blood was the most important driver tissue,and GO analysis suggested that these genes mainly involved in olfactory transduction and erythrocyterelated functions.SMR and TWAS analysis identified three potential causal genes(ABO,HBG1,LINC00886)and their expression in blood,adrenal gland,nerves and other tissues could influence the risk of SM trait.Conclusion Based on largescale GWAS and eQTL data,new SM risk genes HBG1 and LINC00886 were identified which could serve as potential target genes for risk identification and preventive therapy.
作者
龙奇涵
薛超
李淼新
LONG Qi⁃han;XUE Chao;LI Miao⁃xin(Zhongshan School of Medicine,Sun Yat⁃sen University,Guangzhou,Guangdong 510080,China)
出处
《热带医学杂志》
CAS
2022年第4期451-456,463,共7页
Journal of Tropical Medicine
基金
国家自然科学基金(32170637,31970650)
作者简介
龙奇涵(1997-),男,硕士研究生,研究方向:生物信息学与计算生物学;通信作者:李淼新,E⁃mail:limiaoxin@mail.sysu.edu.cn
