A machine learning model using SNPs obtained from a genome-wide association study predicts the onset of vincristine-induced peripheral neuropathy
A machine learning model using SNPs obtained from a genome-wide association study predicts the onset of vincristine-induced peripheral neuropathy
复制标题
使用从全基因组关联研究中获得的 SNP 的机器学习模型可预测长春新碱引起的周围神经病变的发作
DOI：
10.1038/s41397-022-00282-8
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Sato Y
中科院分区：
文献类型：
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作者：
Yamada H;Ohmori R;Okada N;Nakamura S;Kagawa K;Fujii S;Miki H;Ishizawa K;Abe M;Sato Y
阿曲斯汀治疗可能引起周围神经病变。在这项研究中，我们使用全基因组关联研究（GWAS）确定了与长春新碱治疗引起的周围神经病变发展相关的基因，并使用基于遗传信息的机器学习构建了周围神经病变发展的预测模型。该研究纳入了德岛大学医院血液科收治的72例接受长春新碱治疗的患者。其中，使用Illumina Asian Screening Array-24 Kit对56例患者进行基因分型，并对长春新碱引起的周围神经病变发作进行GWAS。使用桑格测序对16个验证样本进行测序，确定了与周围神经病变发病相关的前三个单核苷酸多态性（SNP）。使用统计软件R包“caret”进行机器学习。56个GWAS和16个验证样本分别用作训练集和测试集。使用随机森林、支持向量机、朴素贝叶斯和神经网络算法构建预测模型。根据GWAS，rs 2110179、rs7126100和rs 2076549与长春新碱给药后周围神经病变的发生相关。使用这三个SNP进行机器学习以构建预测模型。使用rs 2110179和rs 2076549的支持向量机和神经网络获得了93.8%的高准确度。因此，可以通过使用与其相关的SNP的机器学习预测模型有效预测长春新碱治疗引起的周围神经病变发展。
Vincristine treatment may cause peripheral neuropathy. In this study, we identified the genes associated with the development of peripheral neuropathy due to vincristine therapy using a genome-wide association study (GWAS) and constructed a predictive model for the development of peripheral neuropathy using genetic information-based machine learning. The study included 72 patients admitted to the Department of Hematology, Tokushima University Hospital, who received vincristine. Of these, 56 were genotyped using the Illumina Asian Screening Array-24 Kit, and a GWAS for the onset of peripheral neuropathy caused by vincristine was conducted. Using Sanger sequencing for 16 validation samples, the top three single nucleotide polymorphisms (SNPs) associated with the onset of peripheral neuropathy were determined. Machine learning was performed using the statistical software R package “caret”. The 56 GWAS and 16 validation samples were used as the training and test sets, respectively. Predictive models were constructed using random forest, support vector machine, naive Bayes, and neural network algorithms. According to the GWAS, rs2110179, rs7126100, and rs2076549 were associated with the development of peripheral neuropathy on vincristine administration. Machine learning was performed using these three SNPs to construct a prediction model. A high accuracy of 93.8% was obtained with the support vector machine and neural network using rs2110179 and rs2076549. Thus, peripheral neuropathy development due to vincristine therapy can be effectively predicted by a machine learning prediction model using SNPs associated with it.