Kinsman Dawson, Hu Jian, Zhang Zhi, Li Gengxin
Department of Mathematics and Statistics, University of Michigan-Dearborn, Dearborn, MI 48128, USA.
Manufacturing Systems Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA.
Genes (Basel). 2024 Mar 26;15(4):407. doi: 10.3390/genes15040407.
Hypophosphatasia is a rare inherited metabolic disorder caused by the deficiency of tissue-nonspecific alkaline phosphatase. More severe and early onset cases present symptoms of muscle weakness, diminished motor coordination, and epileptic seizures. These neurological manifestations are poorly characterized. Thus, it is urgent to discover novel differentially expressed genes for investigating the genetic mechanisms underlying the neurological manifestations of hypophosphatasia. RNA-sequencing data offer a high-resolution and highly accurate transcript profile. In this study, we apply an empirical Bayes model to RNA-sequencing data acquired from the spinal cord and neocortex tissues of a mouse model, individually, to more accurately estimate the genetic effects without bias. More importantly, we further develop two integration methods, weighted gene approach and weighted method, to incorporate two RNA-sequencing data into a model for enhancing the effects of genetic markers in the diagnostics of hypophosphatasia disease. The simulation and real data analysis have demonstrated the effectiveness of our proposed integration methods, which can maximize genetic signals identified from the spinal cord and neocortex tissues, minimize the prediction error, and largely improve the prediction accuracy in risk prediction.
低磷酸酯酶症是一种由组织非特异性碱性磷酸酶缺乏引起的罕见遗传性代谢紊乱疾病。病情更严重且发病较早的病例会出现肌肉无力、运动协调性下降和癫痫发作等症状。这些神经学表现的特征尚不明确。因此，迫切需要发现新的差异表达基因，以研究低磷酸酯酶症神经学表现背后的遗传机制。RNA测序数据提供了高分辨率和高精度的转录本概况。在本研究中，我们将经验贝叶斯模型分别应用于从小鼠模型的脊髓和新皮质组织获取的RNA测序数据，以更准确地估计遗传效应而无偏差。更重要的是，我们进一步开发了两种整合方法，即加权基因法和加权法，将两个RNA测序数据纳入一个模型，以增强遗传标记在低磷酸酯酶症疾病诊断中的作用。模拟和实际数据分析证明了我们提出的整合方法的有效性，该方法可以最大化从脊髓和新皮质组织中识别出的遗传信号，最小化预测误差，并在很大程度上提高风险预测中的预测准确性。
