Functional Genomics Unit, Institute of Genomics and Integrative Biology, CSIR, Delhi, India.
BMC Genomics. 2010 Feb 2;11:84. doi: 10.1186/1471-2164-11-84.
Identification of disease genes for Type 2 Diabetes (T2D) by traditional methods has yielded limited success. Based on our previous observation that T2D may result from disturbed protein-protein interactions affected through disrupting modular domain interactions, here we have designed an approach to rank the candidates in the T2D linked genomic regions as plausible disease genes.
Our approach integrates Weight value (Wv) method followed by prioritization using clustering coefficients derived from domain interaction network. Wv for each candidate is calculated based on the assumption that disease genes might be functionally related, mainly facilitated by interactions among domains of the interacting proteins. The benchmarking using a test dataset comprising of both known T2D genes and non-T2D genes revealed that Wv method had a sensitivity and specificity of 0.74 and 0.96 respectively with 9 fold enrichment. The candidate genes having a Wv > 0.5 were called High Weight Elements (HWEs). Further, we ranked HWEs by using the network property-the clustering coefficient (Ci). Each HWE with a Ci < 0.015 was prioritized as plausible disease candidates (HWEc) as previous studies indicate that disease genes tend to avoid dense clustering (with an average Ci of 0.015). This method further prioritized the identified disease genes with a sensitivity of 0.32 and a specificity of 0.98 and enriched the candidate list by 6.8 fold. Thus, from the dataset of 4052 positional candidates the method ranked 435 to be most likely disease candidates. The gene ontology sharing for the candidates showed higher representation of metabolic and signaling processes. The approach also captured genes with unknown functions which were characterized by network motif analysis.
Prioritization of positional candidates is essential for cost-effective and an expedited discovery of disease genes. Here, we demonstrate a novel approach for disease candidate prioritization from numerous loci linked to T2D.
通过传统方法鉴定 2 型糖尿病（T2D）的疾病基因的方法取得的成果有限。基于我们之前的观察结果，即 T2D 可能是由于受干扰的蛋白质-蛋白质相互作用导致的，这些相互作用通过破坏模块域相互作用而受到影响，在这里我们设计了一种方法，将 T2D 连锁基因组区域中的候选基因按可能的疾病基因进行排序。
我们的方法结合了权重值（Wv）方法，并通过基于域相互作用网络的聚类系数进行优先级排序。根据疾病基因可能通过相互作用蛋白的域之间的相互作用具有功能相关性的假设，为每个候选基因计算 Wv。使用包含已知 T2D 基因和非 T2D 基因的测试数据集进行基准测试，结果表明 Wv 方法的敏感性和特异性分别为 0.74 和 0.96，富集倍数为 9 倍。Wv 值大于 0.5 的候选基因被称为高权重元素（HWE）。此外，我们通过网络属性——聚类系数（Ci）对 HWE 进行排序。每个 Ci 值小于 0.015 的 HWE 被优先考虑为可能的疾病候选基因（HWEc），因为先前的研究表明疾病基因往往避免密集聚类（平均 Ci 值为 0.015）。该方法进一步对已鉴定的疾病基因进行优先级排序，敏感性为 0.32，特异性为 0.98，候选基因列表的富集倍数为 6.8 倍。因此，从 4052 个位置候选基因的数据集中，该方法将 435 个基因列为最有可能的疾病候选基因。候选基因的基因本体共享显示代谢和信号转导过程的代表性更高。该方法还捕获了具有未知功能的基因，这些基因通过网络基元分析进行了特征描述。
对位置候选基因进行优先级排序对于经济高效和加速发现疾病基因至关重要。在这里，我们展示了一种从与 T2D 相关的众多基因座中对疾病候选基因进行优先级排序的新方法。
