Purpose

Roughly 70% of suspected Mendelian disease patients remain undiagnosed after genome sequencing, partly because knowledge about pathogenic genes is incomplete and constantly growing. Generating a novel pathogenic gene hypothesis from patient data can be time-consuming especially where cohort-based analysis is not available.

Methods

Each patient genome contains dozens to hundreds of candidate variants. Many sources of indirect evidence about each candidate may be considered. We introduce InpherNet, a network-based machine learning approach leveraging Monarch Initiative data to accelerate this process.

Results

InpherNet ranks candidate genes based on orthologs, paralogs, functional pathway members, and colocalized interaction partner gene neighbors. It can propose novel pathogenic genes and reveal known pathogenic genes whose diagnosed patient-based annotation is missing or partial. InpherNet is applied to patient cases where the causative gene is incorrectly ranked low by clinical gene-ranking methods that use only patient-derived evidence. InpherNet correctly ranks the causative gene top 1 or top 1–5 in roughly twice as many cases as seven comparable tools, including in cases where no clinical evidence for the diagnostic gene is in our knowledgebase.

Conclusion

InpherNet improves the state of the art in considering candidate gene neighbors to accelerate monogenic diagnosis.

中文翻译：

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InpherNet 利用患者候选基因的邻居加速单基因疾病诊断

目的

大约 70% 的疑似孟德尔病患者在基因组测序后仍未得到诊断，部分原因是有关致病基因的知识不完整且不断增长。从患者数据中生成新的致病基因假设可能非常耗时，尤其是在无法进行基于队列的分析的情况下。

方法

每个患者基因组包含数十到数百个候选变体。可以考虑有关每个候选人的许多间接证据来源。我们介绍了 InpherNet，这是一种基于网络的机器学习方法，利用 Monarch Initiative 数据来加速这一过程。

结果

InpherNet 根据直系同源物、旁系同源物、功能通路成员和共定位的相互作用伙伴基因邻居对候选基因进行排名。它可以提出新的致病基因并揭示其诊断的基于患者的注释缺失或部分的已知致病基因。InpherNet 应用于仅使用患者衍生证据的临床基因排序方法错误地将致病基因排在低位的患者病例。InpherNet 正确地将致病基因排在前 1 或前 1-5 的情况下，大约是七种可比工具的两倍，包括在我们的知识库中没有诊断基因的临床证据的情况下。

结论

InpherNet 在考虑候选基因邻居以加速单基因诊断方面改进了现有技术。