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.

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。当前的算法使用各种语义和统计方法来对具有候选致病变异的通常很长的基因列表进行优先级排序。这些算法不提供对排名列表中的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结果的贡献程度的测量。然而，鉴于在许多队列中基因组诊断的总体成功率仅为 25%–50% 左右或更低，良好的排名并不能意味着排名第一的基因或疾病一定是好的候选者。在这里，我们提出了一种基因组诊断方法，利用似然比 (LR) 框架来估计 (1) 候选诊断的后测概率，(2) 每个观察到的 HPO 表型的 LR，以及 (3) 预测值观察到的基因型的致病性。临床异常似然比解释（LIRICAL）在包含 262 种孟德尔疾病的 384 份病例报告中将正确诊断率排在前三名，正确诊断的平均后测概率为 67.3%。模拟表明，LIRICAL 对于许多常见形式的基因组和表型噪声具有鲁棒性。总之，LIRICAL 为表型驱动的基因组诊断提供了准确的、临床可解释的结果。