Network Properties of Complex Human Disease Genes Identified through Genome-Wide Association Studies

BackgroundPrevious studies of network properties of human disease genes have mainly focused on monogenic diseases or cancers and have suffered from discovery bias. Here we investigated the network properties of complex disease genes identified by genome-wide association studies (GWAs), thereby eliminating discovery bias.
Principal findingsWe derived a network of complex diseases (n = 54) and complex disease genes (n = 349) to explore the shared genetic architecture of complex diseases. We evaluated the centrality measures of complex disease genes in comparison with essential and monogenic disease genes in the human interactome. The complex disease network showed that diseases belonging to the same disease class do not always share common disease genes. A possible explanation could be that the variants with higher minor allele frequency and larger effect size identified using GWAs constitute disjoint parts of the allelic spectra of similar complex diseases. The complex disease gene network showed high modularity with the size of the largest component being smaller than expected from a randomized null-model. This is consistent with limited sharing of genes between diseases. Complex disease genes are less central than the essential and monogenic disease genes in the human interactome. Genes associated with the same disease, compared to genes associated with different diseases, more often tend to share a protein-protein interaction and a Gene Ontology Biological Process.
ConclusionsThis indicates that network neighbors of known disease genes form an important class of candidates for identifying novel genes for the same disease.

研究背景
既往针对人类疾病基因网络属性的相关研究多聚焦于单基因疾病或癌症，且普遍存在发现偏倚问题。本研究针对通过全基因组关联研究（GWAs）鉴定的复杂疾病基因开展网络属性分析，从而规避了发现偏倚。

主要研究结果
本研究构建了包含54种复杂疾病与349个复杂疾病基因的互作网络，以探究复杂疾病共有的遗传架构。我们在人类相互作用组中对比分析了复杂疾病基因、必需基因与单基因疾病基因的中心性指标。结果显示，归属于同一疾病类别的复杂疾病并非总能共享共同的致病基因，一种可能的解释是：通过GWAs鉴定得到的次要等位基因频率更高、效应量更大的变异，分布于相似复杂疾病等位基因谱的不相交区域。复杂疾病基因网络呈现出较高的模块化特征，其最大连通分量的规模小于随机零模型的预期值，这与不同疾病间致病基因共享程度有限的结论相符。在人类相互作用组中，复杂疾病基因的中心性低于必需基因与单基因疾病基因。相较于与不同疾病相关的基因，同一疾病关联的基因更倾向于共享蛋白质相互作用与基因本体论生物学过程（Gene Ontology Biological Process）。

研究结论
上述结果表明，已知疾病基因的网络邻接基因可作为筛选同一疾病新型致病基因的重要候选类别。