Department of Computer Science, Stanford University, Stanford, California, 94305, USA.
Department of Pediatrics, Stanford University, Stanford, California, 94305, USA.
Genet Med. 2019 Feb;21(2):464-470. doi: 10.1038/s41436-018-0072-y. Epub 2018 Jul 12.
Exome sequencing and diagnosis is beginning to spread across the medical establishment. The most time-consuming part of genome-based diagnosis is the manual step of matching the potentially long list of patient candidate genes to patient phenotypes to identify the causative disease.
We introduce Phrank (for phenotype ranking), an information theory-inspired method that utilizes a Bayesian network to prioritize candidate diseases or genes, as a stand-alone module that can be run with any underlying knowledgebase and any variant filtering scheme.
Phrank outperforms existing methods at ranking the causative disease or gene when applied to 169 real patient exomes with Mendelian diagnoses. Phrank's greatest improvement is in disease space, where across all 169 patients it ranks only 3 diseases on average ahead of the true diagnosis, whereas Phenomizer ranks 32 diseases ahead of the causal one.
Using Phrank to rank all patient candidate genes or diseases, as they start working through a new case, will save the busy clinician much time in deriving a genetic diagnosis.
外显子组测序和诊断开始在医疗领域普及。基于基因组的诊断中最耗时的部分是手动将潜在的长列表的患者候选基因与患者表型相匹配，以确定致病疾病。
我们引入了 Phrank（用于表型排序），这是一种受信息论启发的方法，它利用贝叶斯网络对候选疾病或基因进行优先级排序，作为一个独立的模块，可以与任何基础知识库和任何变体过滤方案一起运行。
当应用于 169 例具有孟德尔诊断的真实患者外显子时，Phrank 在对致病疾病或基因进行排序方面优于现有方法。Phrank 的最大改进是在疾病空间中，在所有 169 名患者中，它的平均排名仅比真正的诊断提前 3 种疾病，而 Phenomizer 则提前 32 种疾病。
在临床医生开始处理新病例时，使用 Phrank 对所有患者候选基因或疾病进行排序，将为忙碌的临床医生节省大量时间来得出遗传诊断。
