Interpretable Clinical Genomics with a Likelihood Ratio Paradigm
Interpretable Clinical Genomics with a Likelihood Ratio Paradigm
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DOI：
10.1016/j.ajhg.2020.06.021
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发表时间：
2020-09-03
影响因子：
9.8
通讯作者：
Smedley, Damian
中科院分区：
文献类型：
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作者：
Robinson, Peter N.;Ravanmehr, Vida;Smedley, Damian
基于人类表型本体(HPO)的分析已成为罕见疾病基因组诊断的标准。目前的算法使用各种语义和统计方法来确定具有候选致病变异的典型的长基因列表的优先顺序。这些算法不提供对排名列表中位置之外的预测强度的稳健估计，也不提供任何单个表型观察对优先排序结果的贡献程度的测量。然而，考虑到基因组诊断的总体成功率在许多队列中只有25%-50%或更低，一个好的排名不能意味着排名第一的基因或疾病一定是一个好的候选。在这里，我们提出了一种基因组诊断的方法，它利用似然比(LR)框架来提供(1)候选诊断的测试后概率，(2)每个观察到的HPO表型的似然比，以及(3)观察到的基因型的预测致病性的估计。《临床异常的似然比解释》(LIRICAL)在384个病例报告(包括262个孟德尔疾病)中，92.9%的正确诊断位于前三位，正确诊断的平均后测概率为67.3%。模拟表明，LIRICAL对许多常见的基因组和表观噪声形式具有很强的鲁棒性。总之，LIRICAL为表型驱动的基因组诊断提供了准确的、临床上可解释的结果。
Human Phenotype Ontology (HPO)-based analysis has become standard for genomic diagnostics of rare diseases. Current algorithms use a variety of semantic and statistical approaches to prioritize the typically long lists of genes with candidate pathogenic variants. These algorithms do not provide robust estimates of the strength of the predictions beyond the placement in a ranked list, nor do they provide measures of how much any individual phenotypic observation has contributed to the prioritization result. However, given that the overall success rate of genomic diagnostics is only around 25%-50% or less in many cohorts, a good ranking cannot be taken to imply that the gene or disease at rank one is necessarily a good candidate. Here, we present an approach to genomic diagnostics that exploits the likelihood ratio (LR) framework to provide an estimate of (1) the posttest probability of candidate diagnoses, (2) the LR for each observed HPO phenotype, and (3) the predicted pathogenicity of observed genotypes. LIkelihood Ratio Interpretation of Clinical AbnormaLities (LIRICAL) placed the correct diagnosis within the first three ranks in 92.9% of 384 case reports comprising 262 Mendelian diseases, and the correct diagnosis had a mean posttest probability of 67.3%. Simulations show that LIRICAL is robust to many typically encountered forms of genomic and phenomic noise. In summary, LIRICAL provides accurate, clinically interpretable results for phenotype-driven genomic diagnostics.