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目的以WTCCCⅠ期公开发表的高血压SNPs数据作为实际数据,展开基于MAX的生物学通路分析,完成该方法的实现过程,并探讨其优势。方法运用Python语言和PLINK软件对数据进行格式转换,应用MAX方法进行单个SNP位点的关联分析,通过i-GSEA4GWAS网络分析平台进行通路分析,寻找疾病的差异表达生物学通路。结果共筛选出6条差异表达通路,查阅文献发现2条差异表达通路与高血压有直接关系,2条差异表达通路与高血压有间接关系。结论在疾病的遗传模型未知的情况下,基于MAX的生物学通路分析综合考虑了多个遗传模型的信息,是一种高效且稳健的分析方法。
OBJECTIVE To investigate the prevalence of hypertension based on SNP data published in the first phase of WTCCC. Methods Using Python language and PLINK software to convert the data, a single SNP locus was analyzed by MAX method, and path analysis was conducted by i-GSEA4GWAS network analysis platform to find the pathways of differential expression of disease. Results A total of 6 differentially expressed pathways were screened out. It was found that the two differentially expressed pathways were directly related to hypertension and the two differentially expressed pathways were indirectly related to hypertension. Conclusions In the absence of genetic models of disease, the MAX-based biological pathway analysis is a highly efficient and robust analytical method that takes into account the information from multiple genetic models.