Liang Zhaohui, Liu Jun, Huang Jimmy X, Zeng Xing
Information Retrieval and Knowledge Management Research Lab, School of Information Technology, York University, Toronto, ON, M3J 1P3, Canada.
Second School of Clinic Medicine, Guangzhou University of Chinese Medicine, Guanzhou 510120, China.
Comb Chem High Throughput Screen. 2018;21(2):93-99. doi: 10.2174/1386207321666180115094814.
The genetic polymorphism of Cytochrome P450 (CYP 450) is considered as one of the main causes for adverse drug reactions (ADRs). In order to explore the latent correlations between ADRs and potentially corresponding single-nucleotide polymorphism (SNPs) in CYP450, three algorithms based on information theory are used as the main method to predict the possible relation.
The study uses a retrospective case-control study to explore the potential relation of ADRs to specific genomic locations and single-nucleotide polymorphism (SNP). The genomic data collected from 53 healthy volunteers are applied for the analysis, another group of genomic data collected from 30 healthy volunteers excluded from the study are used as the control group. The SNPs respective on five loci of CYP2D6*2,*10,14 and CYP1A21C, *1F are detected by the Applied Biosystem 3130xl. The raw data is processed by ChromasPro to detect the specific alleles on the above loci from each sample. The secondary data are reorganized and processed by R combined with the reports of ADRs from clinical reports. Three information theory based algorithms are implemented for the screening task: JMI, CMIM, and mRMR. If a SNP is selected by more than two algorithms, we are confident to conclude that it is related to the corresponding ADR. The selection results are compared with the control decision tree + LASSO regression model.
In the study group where ADRs occur, 10 SNPs are considered relevant to the occurrence of a specific ADR by the combined information theory model. In comparison, only 5 SNPs are considered relevant to a specific ADR by the decision tree + LASSO regression model. In addition, the new method detects more relevant pairs of SNP and ADR which are affected by both SNP and dosage. This implies that the new information theory based model is effective to discover correlations of ADRs and CYP 450 SNPs and is helpful in predicting the potential vulnerable genotype for some ADRs.
The newly proposed information theory based model has superiority performance in detecting the relation between SNP and ADR compared to the decision tree + LASSO regression model. The new model is more sensitive to detect ADRs compared to the old method, while the old method is more reliable. Therefore, the selection criteria for selecting algorithms should depend on the pragmatic needs.
细胞色素P450（CYP 450）的基因多态性被认为是药物不良反应（ADR）的主要原因之一。为了探究ADR与CYP450中潜在的相应单核苷酸多态性（SNP）之间的潜在关联，使用三种基于信息论的算法作为主要方法来预测可能的关系。
本研究采用回顾性病例对照研究，以探究ADR与特定基因组位置和单核苷酸多态性（SNP）之间的潜在关系。将从53名健康志愿者收集的基因组数据用于分析，另一组从30名被排除在研究之外的健康志愿者收集的基因组数据用作对照组。通过应用生物系统3130xl检测CYP2D6*2、*10、14以及CYP1A21C、*1F五个位点上的SNP。原始数据由ChromasPro处理，以从每个样本中检测上述位点上的特定等位基因。二次数据通过R结合临床报告中的ADR报告进行重组和处理。实施三种基于信息论的算法进行筛选任务：JMI、CMIM和mRMR。如果一个SNP被两种以上算法选中，我们有信心得出它与相应ADR相关的结论。将选择结果与对照决策树+LASSO回归模型进行比较。
在出现ADR的研究组中，联合信息论模型认为有10个SNP与特定ADR的发生相关。相比之下，决策树+LASSO回归模型仅认为有5个SNP与特定ADR相关。此外，新方法检测到更多受SNP和剂量共同影响的SNP与ADR的相关对。这意味着新的基于信息论的模型在发现ADR与CYP 450 SNP的相关性方面是有效的，并且有助于预测某些ADR的潜在易感基因型。
与决策树+LASSO回归模型相比，新提出的基于信息论的模型在检测SNP与ADR之间的关系方面具有优越性能。与旧方法相比，新模型在检测ADR方面更敏感，而旧方法更可靠。因此，选择算法的标准应取决于实际需求。
