Department of Medicine, Mackay Medical College, New Taipei City, Taiwan.
Department of Laboratory Medicine, MacKay Memorial Hospital, Taipei City, Taiwan.
J Transl Med. 2022 Apr 28;20(1):190. doi: 10.1186/s12967-022-03379-7.
The circadian system is responsible for regulating various physiological activities and behaviors and has been gaining recognition. The circadian rhythm is adjusted in a 24-h cycle and has transcriptional-translational feedback loops. When the circadian rhythm is interrupted, affecting the expression of circadian genes, the phenotypes of diseases could amplify. For example, the importance of maintaining the internal temporal homeostasis conferred by the circadian system is revealed as mutations in genes coding for core components of the clock result in diseases. This study will investigate the association between circadian genes and metabolic syndromes in a Taiwanese population.
We performed analysis using whole-genome sequencing, read vcf files and set target circadian genes to determine if there were variants on target genes. In this study, we have investigated genetic contribution of circadian-related diseases using population-based next generation whole genome sequencing. We also used significant SNPs to create a metabolic syndrome prediction model. Logistic regression, random forest, adaboost, and neural network were used to predict metabolic syndrome. In addition, we used random forest model variables importance matrix to select 40 more significant SNPs, which were subsequently incorporated to create new prediction models and to compare with previous models. The data was then utilized for training set and testing set using five-fold cross validation. Each model was evaluated with the following criteria: area under the receiver operating characteristics curve (AUC), precision, F1 score, and average precision (the area under the precision recall curve).
After searching significant variants, we used Chi-Square tests to find some variants. We found 186 significant SNPs, and four predicting models which used 186 SNPs (logistic regression, random forest, adaboost and neural network), AUC were 0.68, 0.8, 0.82, 0.81 respectively. The F1 scores were 0.412, 0.078, 0.295, 0.552, respectively. The other three models which used the 40 SNPs (logistic regression, adaboost and neural network), AUC were 0.82, 0.81, 0.81 respectively. The F1 scores were 0.584, 0.395, 0.574, respectively.
Circadian gene defect may also contribute to metabolic syndrome. Our study found several related genes and building a simple model to predict metabolic syndrome.
生物钟系统负责调节各种生理活动和行为，其重要性日益受到认可。生物钟的节律以 24 小时为周期进行调整，并具有转录-翻译反馈回路。当生物钟节律受到干扰，影响生物钟基因的表达时，疾病的表型可能会放大。例如，编码时钟核心组件的基因突变导致的疾病揭示了维持内部时间稳态的重要性。本研究将在台湾人群中研究生物钟基因与代谢综合征之间的关联。
我们使用全基因组测序进行分析，读取 vcf 文件并设置目标生物钟基因，以确定目标基因上是否存在变体。在这项研究中，我们使用基于人群的下一代全基因组测序研究了与生物钟相关的疾病的遗传贡献。我们还使用显著 SNP 构建了代谢综合征预测模型。逻辑回归、随机森林、自适应增强和神经网络用于预测代谢综合征。此外，我们使用随机森林模型变量重要性矩阵选择了 40 个更显著的 SNP，然后将其纳入新的预测模型，并与之前的模型进行比较。然后，利用该数据通过五折交叉验证进行训练集和测试集。使用以下标准评估每个模型：接收者操作特征曲线下的面积 (AUC)、精度、F1 分数和平均精度（精确召回曲线下的面积）。
在搜索到显著变体后，我们使用卡方检验找到了一些变体。我们发现了 186 个显著 SNP，并使用这 186 个 SNP 构建了四个预测模型（逻辑回归、随机森林、自适应增强和神经网络），AUC 分别为 0.68、0.8、0.82、0.81。F1 分数分别为 0.412、0.078、0.295、0.552。另外三个使用 40 个 SNP 的模型（逻辑回归、自适应增强和神经网络），AUC 分别为 0.82、0.81、0.81。F1 分数分别为 0.584、0.395、0.574。
生物钟基因缺陷也可能导致代谢综合征。我们的研究发现了一些相关基因，并构建了一个简单的模型来预测代谢综合征。
