Network properties of complex human disease genes identified through genome-wide association studies.
Network properties of complex human disease genes identified through genome-wide association studies.
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DOI：
10.1371/journal.pone.0008090
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发表时间：
2009-11-30
期刊：
影响因子：
3.7
通讯作者：
Benson M
中科院分区：
文献类型：
--
作者：
Barrenas F;Chavali S;Holme P;Mobini R;Benson M
以往对人类疾病基因网络特性的研究主要集中在单基因疾病或癌症上，存在发现偏差。在这里，我们研究了由全基因组关联研究（GWA）确定的复杂疾病基因的网络特性，从而消除了发现偏差。我们推导了一个复杂疾病（n = 54）和复杂疾病基因（n = 349）的网络，以探索复杂疾病的共享遗传结构。 我们评估了复杂的疾病基因的中心性措施相比，人类相互作用组中的基本和单基因疾病基因。复杂的疾病网络表明，属于同一疾病类别的疾病并不总是共享共同的疾病基因。一种可能的解释是，使用GWA鉴定的具有较高次要等位基因频率和较大效应大小的变体构成了类似复杂疾病的等位基因谱的不相交部分。复杂的疾病基因网络显示出高模块化，最大组件的大小小于随机零模型的预期。这与疾病之间有限的基因共享是一致的。在人类相互作用组中，复杂疾病基因不如必需和单基因疾病基因重要。与不同疾病相关的基因相比，与同一疾病相关的基因往往更倾向于共享蛋白质-蛋白质相互作用和基因本体生物学过程。这表明已知疾病基因的网络邻居形成了用于识别相同疾病的新基因的重要候选类别。
Previous 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. We 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. This indicates that network neighbors of known disease genes form an important class of candidates for identifying novel genes for the same disease.
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基于网络的人类疾病基因的全局推断。
影响因子：
9.9
作者：
Wu, Xuebing;Jiang, Rui;Zhang, Michael Q.;Li, Shao
通讯作者：
Li, Shao
影响因子：
14.9
作者：
Keshava Prasad TS;Goel R;Kandasamy K;Keerthikumar S;Kumar S;Mathivanan S;Telikicherla D;Raju R;Shafreen B;Venugopal A;Balakrishnan L;Marimuthu A;Banerjee S;Somanathan DS;Sebastian A;Rani S;Ray S;Harrys Kishore CJ;Kanth S;Ahmed M;Kashyap MK;Mohmood R;Ramachandra YL;Krishna V;Rahiman BA;Mohan S;Ranganathan P;Ramabadran S;Chaerkady R;Pandey A
通讯作者：
Pandey A
影响因子：
14.9
作者：
Blake JA;Bult CJ;Eppig JT;Kadin JA;Richardson JE;Mouse Genome Database Group
通讯作者：
Mouse Genome Database Group
影响因子：
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作者：
Craft, Suzanne
通讯作者：
Craft, Suzanne
影响因子：
64.8
作者：
Jeong, H;Mason, SP;Oltvai, ZN
通讯作者：
Oltvai, ZN