基于改进的多元关联分析模型的多种精神疾病相关基因识别  

Identification of multiple psychiatric disorders correlated genes based on an improved multivariate association analysis model

作　　者：赵国英 贺平安[1] ZHAO Guoying;HE Ping'an(School of Science,Zhejiang Sci-Tech University,Hangzhou,310018,China)

机构地区：[1]浙江理工大学理学院,杭州310018

出　　处：《浙江理工大学学报（自然科学版）》2022年第6期923-930,共8页Journal of Zhejiang Sci-Tech University(Natural Sciences)

基　　金：国家自然科学基金项目(61772027)。

摘　　要：基于基因的多元关联分析(Multivariate gene-based association analysis,MGAS)模型能有效地识别基因与表型之间的相关性,然而现有MGAS模型在多元关联分析时会剔除很多相关性数值个数小于疾病种类数目的单核苷酸多态性(Single nucleotide polymorphism,SNP)位点。针对多元关联分析中潜在SNP位点缺失的问题,利用数据填充的方法改进了MGAS模型,并将其应用于6类精神疾病的基因与表型相关性的识别。通过对比改进的MGAS模型与原有模型得到的Top显著基因发现,改进的MGAS模型提高了多元关联分析与疾病相关基因的识别能力,有助于发现多种疾病间的潜在的风险基因,为疾病的预防、诊断和治疗研究提供新的工具和思路。Multivariate gene-based association analysis(MGAS)model can effectively identify the genotypes-phenotypes association.However,many single nucleotide polymorphism(SNP)loci with the number of correlation values being less than the number of disease were deleted in the multivariate association analysis of the existing MGAS model.An improved MGAS model based on data completion method was proposed to solve the problem of missing potential SNP loci in multivariate association analysis.And it was applied to identify the genotypes-phenotypes association in six psychiatric disorders.The results of the improved MGAS model and the original model showed that the improved MGAS model improved the identification ability of the genotypes-phenotypes association.The work is conducive to discovering the potential risk genes among various diseases and provide new ideas for disease prevention,diagnosis and treatment.

关 键 词：精神疾病 多元关联分析 MGAS模型 显著基因 数据填充 

分 类 号：O29[理学—应用数学]