Network-Based Prediction of Polygenic Disease Genes Involved in Cell Motility: Extended Abstract
Network-Based Prediction of Polygenic Disease Genes Involved in Cell Motility: Extended Abstract
复制标题
涉及细胞运动的多基因疾病基因的基于网络的预测：扩展摘要
DOI：
10.1145/3233547.3233697
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Ritz, Anna
中科院分区：
文献类型：
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作者：
Bern, Miriam;King, Alexander;Applewhite, Derek A.;Ritz, Anna
精神分裂症和自闭症是由多种遗传变异引起的多基因疾病的例子。最近，这两种疾病都与破坏神经元运动和迁移模式有关，表明异常细胞运动是这些神经系统疾病的表型。神经元发育异常是精神分裂症和自闭症的核心，这严重地暗示了这些细胞运动扰动在疾病机制中。然而，尽管通过大规模全基因组关联研究对这些疾病进行了遗传表征，但由于涉及大量基因以及突变对细胞过程的累加效应，从这些数据中提取症状和病理生理学的因果关系仍然具有挑战性。我们提出了一种基于网络的机器学习方法来识别与感兴趣的疾病（例如，精神分裂症或孤独症）和疾病表型（例如，异常细胞运动性）。我们使用脑特异性功能相互作用网络，以确定哪些基因是最核心的多基因疾病的基础上功能相似性牵连。我们的算法识别出网络中与已知疾病基因和细胞运动基因接近的基因。精神分裂症的主要候选基因包括许多蛋白磷酸酶1亚基和赖氨酰氧化酶，它们是有希望进行后续实验验证的基因。通过我们的方法预测的候选基因提出了关于这些基因在细胞运动调节中的作用的可检验的假设，为生成实验验证的预测提供了框架。
Schizophrenia and autism are examples of polygenic diseases caused by a multitude of genetic variants. Recently, both diseases have been associated with disrupted neuron motility and migration patterns, suggesting that aberrant cell motility is a phenotype for these neurological diseases. Abnormal neuronal development is central to both schizophrenia and autism, which critically implicates these cell motility perturbations in the disease mechanisms. However, despite the genetic characterization of these diseases by large-scale genome-wide association studies, extracting causality for symptoms and pathophysiology from these data remains challenging due to the large number of genes implicated and the additive effect the mutations have on the cellular processes. We present a network-based machine learning approach to identify genes implicated in both a disease of interest (e.g., schizophrenia or autism) and a disease phenotype (e.g., aberrant cell motility). We use a brain-specific functional interaction network to identify which genes are most centrally implicated in a polygenic disease based on functional similarity. Our algorithm identifies genes that are near known disease genes and cell motility genes in the network. Top schizophrenia candidates include many Protein Phosphatase 1 subunits and Lysyl Oxidase, which are promising genes for follow-up experimental validation. Candidate genes predicted by our method suggest testable hypotheses about these genes' role in cell motility regulation, offering a framework for generating predictions for experimental validation.