Brain-specific functional relationship networks inform autism spectrum disorder gene prediction
Brain-specific functional relationship networks inform autism spectrum disorder gene prediction
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DOI：
10.1038/s41398-018-0098-6
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发表时间：
2018-03-06
影响因子：
6.8
通讯作者：
Guan, Yuanfang
中科院分区：
文献类型：
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作者：
Duda, Marlena;Zhang, Hongjiu;Guan, Yuanfang
自闭症谱系障碍（ASD）是一种神经精神障碍，有强有力的证据表明遗传贡献，越来越多的研究工作导致ASD候选基因的名单不断增加。然而，在数百个提名的ASD相关基因中，只有一小部分已经确定了可直接归因于该疾病的从头或传递性功能丧失（LOF）突变。出于这个原因，优先考虑ASD候选基因的方法将有助于过滤假阳性结果，并允许研究人员专注于更有可能致病的基因。在这里，我们通过利用基因的大脑特异性功能关系网络（FRN）构建了一个机器学习模型，以产生ASD风险基因的全基因组排名。我们使用来自两个独立测序实验的结果严格验证了我们的基因排名，这些实验共同代表了5000多个单纯和多重ASD家族。最后，通过对我们高度优先的候选基因网络进行功能富集分析，我们确定了少数在早期神经发育中起关键作用的途径，为它们在ASD中的潜在作用提供了进一步的支持。
Autism spectrum disorder (ASD) is a neuropsychiatric disorder with strong evidence of genetic contribution, and increased research efforts have resulted in an ever-growing list of ASD candidate genes. However, only a fraction of the hundreds of nominated ASD-related genes have identified de novo or transmitted loss of function (LOF) mutations that can be directly attributed to the disorder. For this reason, a means of prioritizing candidate genes for ASD would help filter out false-positive results and allow researchers to focus on genes that are more likely to be causative. Here we constructed a machine learning model by leveraging a brain-specific functional relationship network (FRN) of genes to produce a genome-wide ranking of ASD risk genes. We rigorously validated our gene ranking using results from two independent sequencing experiments, together representing over 5000 simplex and multiplex ASD families. Finally, through functional enrichment analysis on our highly prioritized candidate gene network, we identified a small number of pathways that are key in early neural development, providing further support for their potential role in ASD.