The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA.
The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
Am J Hum Genet. 2020 Sep 3;107(3):403-417. doi: 10.1016/j.ajhg.2020.06.021. Epub 2020 Aug 4.
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对具有候选致病性变异的基因的长列表进行优先级排序。这些算法除了在排序列表中的位置之外，没有为预测的强度提供稳健的估计，也没有为任何个体表型观察对优先级排序结果的贡献提供度量。然而，鉴于在许多队列中，基因组诊断的总体成功率仅约为 25%-50%或更低，因此良好的排名并不能暗示排名第一的基因或疾病一定是一个很好的候选者。在这里，我们提出了一种基因组诊断方法，该方法利用似然比（LR）框架来提供（1）候选诊断的后验概率，（2）每个观察到的 HPO 表型的 LR，以及（3）观察到的基因型的预测致病性的估计。LIkelihood Ratio Interpretation of Clinical AbnormaLities（LIRICAL）在包含 262 种孟德尔疾病的 384 份病例报告中，有 92.9%将正确的诊断置于前三个等级内，正确诊断的后验概率平均为 67.3%。模拟表明，LIRICAL 对许多通常遇到的基因组和表型噪声形式具有鲁棒性。总之，LIRICAL 为基于表型的基因组诊断提供了准确、具有临床可解释性的结果。
