Department of Computer Science and Engineering.
Faculty of Medicine.
Bioinformatics. 2018 May 15;34(10):1741-1749. doi: 10.1093/bioinformatics/bty005.
Individual genetic variants explain only a small fraction of heritability in some diseases. Some variants have weak marginal effects on disease risk, but their joint effects are significantly stronger when occurring together. Most studies on such epistatic interactions have focused on methods for identifying the interactions and interpreting individual cases, but few have explored their general functional basis. This was due to the lack of a comprehensive list of epistatic interactions and uncertainties in associating variants to genes.
We conducted a large-scale survey of published research articles to compile the first comprehensive list of epistatic interactions in human diseases with detailed annotations. We used various methods to associate these variants to genes to ensure robustness. We found that these genes are significantly more connected in protein interaction networks, are more co-expressed and participate more often in the same pathways. We demonstrate using the list to discover novel disease pathways.
Supplementary data are available at Bioinformatics online.
个体遗传变异仅能解释某些疾病中一小部分的遗传率。一些变异对疾病风险的边际效应较弱，但当它们一起发生时，其联合效应显著增强。大多数关于这种上位性相互作用的研究都集中在识别相互作用和解释个体病例的方法上，但很少有研究探索其一般的功能基础。这是由于缺乏全面的上位性相互作用列表以及将变异与基因关联的不确定性。
我们对已发表的研究文章进行了大规模调查，以编制人类疾病上位性相互作用的第一个全面列表，并进行了详细注释。我们使用各种方法将这些变体与基因相关联，以确保稳健性。我们发现这些基因在蛋白质相互作用网络中连接得更紧密，表达更为一致，并且更频繁地参与相同的途径。我们使用该列表展示了发现新的疾病途径的方法。
补充数据可在“Bioinformatics”在线获取。
