Chen Yang, Xu Rong
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA.
BMC Genomics. 2016 Aug 31;17 Suppl 5(Suppl 5):493. doi: 10.1186/s12864-016-2820-1.
Parkinson disease (PD) is a severe neurodegenerative disease without curative drugs. The highly complex and heterogeneous disease mechanisms are still unclear. Detecting novel PD associated genes not only contributes in revealing the disease pathogenesis, but also facilitates discovering new targets for drugs.
We propose a phenome-based gene prediction strategy to identify disease-associated genes for PD. We integrated multiple disease phenotype networks, a gene functional relationship network, and known PD genes to predict novel candidate genes. Then we investigated the translational potential of the predicted genes in drug discovery.
In a cross validation analysis, the average rank for 15 known PD genes is within top 0.8 %. We also tested the algorithm with an independent validation set of 669 PD-associated genes detected by genome-wide association studies. The top ranked genes predicted by our approach are enriched for these validation genes. In addition, our approach prioritized the target genes for FDA-approved PD drugs and the drugs that have been tested for PD in clinical trials. Pathway analysis shows that the prioritized drug target genes are closely associated with PD pathogenesis. The result provides empirical evidence that our computational gene prediction approach identifies novel candidate genes for PD, and has the potential to lead to rapid drug discovery.
帕金森病（PD）是一种尚无治愈药物的严重神经退行性疾病。其高度复杂且异质性的疾病机制仍不清楚。检测新的帕金森病相关基因不仅有助于揭示疾病发病机制，还能促进发现新的药物靶点。
我们提出了一种基于表型组的基因预测策略来识别帕金森病的疾病相关基因。我们整合了多个疾病表型网络、一个基因功能关系网络和已知的帕金森病基因来预测新的候选基因。然后我们研究了预测基因在药物发现中的转化潜力。
在交叉验证分析中，15个已知帕金森病基因的平均排名在前0.8%以内。我们还用通过全基因组关联研究检测到的669个帕金森病相关基因的独立验证集测试了该算法。我们的方法预测的排名靠前的基因在这些验证基因中富集。此外，我们的方法对FDA批准的帕金森病药物和已在临床试验中针对帕金森病进行测试的药物的靶基因进行了优先排序。通路分析表明，优先排序的药物靶基因与帕金森病发病机制密切相关。结果提供了经验证据，表明我们的计算基因预测方法能够识别帕金森病的新候选基因，并有可能实现快速药物发现。
