TCS Research and Innovation, Hyderabad, 500081, India.
BMC Med Genomics. 2018 Jul 6;11(1):57. doi: 10.1186/s12920-018-0372-8.
One of the major goals of genomic medicine is the identification of causal genomic variants in a patient and their relation to the observed clinical phenotypes. Prioritizing the genomic variants by considering only the genotype information usually identifies a few hundred potential variants. Narrowing it down further to find the causal disease genes and relating them to the observed clinical phenotypes remains a significant challenge, especially for rare diseases.
We propose a phenotype-driven gene prioritization approach using heterogeneous networks in the context of rare diseases. Towards this, we first built a heterogeneous network consisting of ontological associations as well as curated associations involving genes, diseases, phenotypes and pathways from multiple sources. Motivated by the recent progress in spectral graph convolutions, we developed a graph convolution based technique to infer new phenotype-gene associations from this initial set of associations. We included these inferred associations in the initial network and termed this integrated network HANRD (Heterogeneous Association Network for Rare Diseases). We validated this approach on 230 recently published rare disease clinical cases using the case phenotypes as input.
When HANRD was queried with the case phenotypes as input, the causal genes were captured within Top-50 for more than 31% of the cases and within Top-200 for more than 56% of the cases. The results showed improved performance when compared to other state-of-the-art tools.
In this study, we showed that the heterogeneous network HANRD, consisting of curated, ontological and inferred associations, helped improve causal gene identification in rare diseases. HANRD allows future enhancements by supporting incorporation of new entity types and additional information sources.
基因组医学的主要目标之一是识别患者中与观察到的临床表型相关的因果基因组变异。仅考虑基因型信息对基因组变异进行优先级排序通常可以识别出几百个潜在的变异。进一步将其缩小范围以找到与观察到的临床表型相关的致病疾病基因仍然是一个重大挑战，尤其是对于罕见疾病。
我们提出了一种基于表型的基因优先级排序方法，该方法在罕见疾病的背景下使用异构网络。为此，我们首先构建了一个异构网络，该网络由来自多个来源的基因、疾病、表型和途径的本体论关联以及精心策划的关联组成。受最近在光谱图卷积方面取得的进展的启发，我们开发了一种基于图卷积的技术，从这个初始关联集推断新的表型-基因关联。我们将这些推断出的关联纳入初始网络中，并将此集成网络命名为 HANRD（用于罕见疾病的异构关联网络）。我们使用 230 个最近发表的罕见病临床病例的病例表型作为输入，对该方法进行了验证。
当 HANRD 以病例表型作为输入进行查询时，对于超过 31%的病例，因果基因在前 50 名中被捕获；对于超过 56%的病例，因果基因在前 200 名中被捕获。与其他最先进的工具相比，该方法的结果显示出了改进的性能。
在这项研究中，我们表明由精心策划、本体和推断关联组成的异构网络 HANRD 有助于提高罕见疾病中的因果基因识别。HANRD 通过支持新实体类型和其他信息源的纳入，为未来的增强提供了可能。
