Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA.
Methods Mol Biol. 2021;2190:229-248. doi: 10.1007/978-1-0716-0826-5_11.
A fundamental question in precision medicine is to quantitatively decode the genetic basis of complex human diseases, which will enable the development of predictive models of disease risks based on personal genome sequences. To account for the complex systems within different cellular contexts, large-scale regulatory networks are critical components to be integrated into the analysis. Based on the fast accumulation of multiomics and disease genetics data, advanced machine learning algorithms and efficient computational tools are becoming the driving force in predicting phenotypes from genotypes, identifying potential causal genetic variants, and revealing disease mechanisms. Here, we review the state-of-the-art methods for this topic and describe a computational pipeline that assembles a series of algorithms together to achieve improved disease genetics prediction through the delineation of regulatory circuitry step by step.
精准医学中的一个基本问题是定量解码复杂人类疾病的遗传基础，这将使基于个人基因组序列的疾病风险预测模型的开发成为可能。为了考虑到不同细胞环境中的复杂系统，大规模调控网络是整合到分析中的关键组成部分。基于多组学和疾病遗传学数据的快速积累，先进的机器学习算法和高效的计算工具正成为从基因型预测表型、识别潜在因果遗传变异和揭示疾病机制的驱动力。在这里，我们综述了该主题的最新方法，并描述了一个计算流程，该流程将一系列算法组装在一起，通过逐步描绘调控回路来实现疾病遗传学预测的改进。
