Center for Computational Biology and Bioinformatics and Department of Biomedical Informatics, Columbia University, 1130 St. Nicolas Ave, New York, NY 10032, USA.
Neuron. 2011 Jun 9;70(5):898-907. doi: 10.1016/j.neuron.2011.05.021.
Identification of complex molecular networks underlying common human phenotypes is a major challenge of modern genetics. In this study, we develop a method for network-based analysis of genetic associations (NETBAG). We use NETBAG to identify a large biological network of genes affected by rare de novo CNVs in autism. The genes forming the network are primarily related to synapse development, axon targeting, and neuron motility. The identified network is strongly related to genes previously implicated in autism and intellectual disability phenotypes. Our results are also consistent with the hypothesis that significantly stronger functional perturbations are required to trigger the autistic phenotype in females compared to males. Overall, the presented analysis of de novo variants supports the hypothesis that perturbed synaptogenesis is at the heart of autism. More generally, our study provides proof of the principle that networks underlying complex human phenotypes can be identified by a network-based functional analysis of rare genetic variants.
鉴定常见人类表型背后的复杂分子网络是现代遗传学的主要挑战。在这项研究中,我们开发了一种基于网络的遗传关联分析方法(NETBAG)。我们使用 NETBAG 来鉴定自闭症中受罕见新生 CNV 影响的大型基因生物学网络。形成网络的基因主要与突触发育、轴突靶向和神经元运动有关。鉴定出的网络与先前与自闭症和智力障碍表型相关的基因密切相关。我们的结果也与假设一致,即与男性相比,女性需要更强的功能干扰才能引发自闭症表型。总的来说,对新生变异的分析支持这样一种假设,即突触发生的改变是自闭症的核心。更广泛地说,我们的研究提供了证据,证明可以通过对罕见遗传变异的基于网络的功能分析来鉴定复杂人类表型的基础网络。