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Research Article

整合突变和表达数据识别2型糖尿病相关基因特征

卷 22, 期 1, 2022

发表于: 07 July, 2021

页: [51 - 58] 页: 8

弟呕挨: 10.2174/1566523221666210707140839

价格: $65

摘要

病人。随着测序技术的发展，从表达数据中鉴定出了大量的差异表达基因。然而，机器学习的方法只能识别局部最优解作为签名。 目的:通过遗传获得的突变信息能更好地反映基因与疾病之间的关系。因此，我们需要整合突变信息来更准确地识别签名。 为此，我们整合Genome-Wide Association Study (GWAS)数据和表达数据，并结合表达定量性状位点(expression Quantitative Trait Loci, eQTL)技术，得到T2DM预测特征(t2dmsg -10)。首先，我们利用GWAS数据得到T2DM易感基因座列表。然后，利用eQTL技术获取风险单核苷酸多态性(SNPs)，并结合胰腺β-细胞基因表达数据获得10个蛋白编码基因。接下来，我们将这些基因以相同的重量组合在一起。 结果:采用受试者工作特征(Receiver Operating Characteristic, ROC)、单基因去除与增加法、基因本体论功能富集法和蛋白-蛋白相互作用网络验证结果显示，t2dmsg -10对T2DM具有良好的预测效果(AUC=0.99)，且具有较高的鲁棒性。 结论:总之，我们获得了T2DM的预测特征，并进一步进行了验证。

关键词: 2型糖尿病，全基因组关联研究，表达定量性状位点，预测特征，AUC=0.99, ROC。

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