Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network
Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network
复制标题
DOI：
10.1016/j.ajhg.2021.08.010
复制
发表时间：
2021-10-08
影响因子：
9.8
通讯作者：
Capra, John A.
中科院分区：
文献类型：
--
作者：
Mukherjee, Souhrid;Cogan, Joy D.;Capra, John A.
罕见疾病影响着全球数百万人，发现其遗传原因具有挑战性。在未诊断疾病网络（UDN）分析的患者中，有一半以上仍未确诊。这项工作的中心假设是，许多这些罕见的遗传疾病是由一个以上基因的多个变异引起的。然而，考虑到每个个体基因组中的大量变体，实验评估变体组合导致疾病的可能性目前是不可行的。为了应对这一挑战，我们开发了双基因预测器（DiGePred），这是一种随机森林分类器，用于通过来自生物网络、基因组学、进化历史和功能注释的特征来识别候选双基因疾病基因对。我们通过使用DIDA训练DiGePred分类器，DIDA是已知双基因疾病引起基因对的最大可用数据库，以及几组非双基因对，包括来自UDN个体的未受影响亲属的变体对。DiGePred在交叉验证和保留测试集上实现了高精度和召回率（PR曲线下面积> 77%），我们通过使用最近文献中的双基因对进一步证明了其实用性。与其他方法相比，DiGePred在实际临床环境中应用时还适当控制了假阳性的数量。最后，为了能够快速筛选双基因疾病潜力的变异基因对，我们免费提供了DiGePred对所有人类基因对的预测。我们的工作通过提供一种快速评估变异基因对导致双基因疾病的可能性的方法，使发现罕见非单基因疾病的遗传原因成为可能。
Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating combinations of variants for potential to cause disease is currently infeasible. To address this challenge, we developed the digenic predictor (DiGePred), a random forest classifier for identifying candidate digenic disease gene pairs by features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier by using DIDA, the largest available database of known digenic-disease-causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN individuals. DiGePred achieved high precision and recall in cross-validation and on a held-out test set (PR area under the curve > 77%), and we further demonstrate its utility by using digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings. Finally, to enable the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work enables the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.