Background:The proliferation of genetic profiling has revealed many associations between genetic variations and disease. However, large-scale phenotyping efforts in largely healthy populations, coupled with DNA sequencing, suggest variants currently annotated as pathogenic are more common in healthy populations than previously thought. In addition, novel and rare variants are frequently observed in genes associated with disease both in healthy individuals and those under suspicion of disease. This raises the question of whether these variants can be useful predictors of disease. To answer this question, we assessed the degree to which the presence of a variant in the cardiac potassium channel gene KCNH2 was diagnostically predictive for the autosomal dominant long QT syndrome.Methods:We estimated the probability of a long QT diagnosis given the presence of each KCNH2 variant using Bayesian methods that incorporated variant features such as changes in variant function, protein structure, and in silico predictions. We call this estimate the posttest probability of disease. Our method was applied to over 4000 individuals heterozygous for 871 missense or in-frame insertion/deletion variants in KCNH2 and validated against a separate international cohort of 933 individuals heterozygous for 266 missense or in-frame insertion/deletion variants.Results:Our method was well-calibrated for the observed fraction of heterozygotes diagnosed with long QT syndrome. Heuristically, we found that the innate diagnostic information one learns about a variant from 3-dimensional variant location, in vitro functional data, and in silico predictors is equivalent to the diagnostic information one learns about that same variant by clinically phenotyping 10 heterozygotes. Most importantly, these data can be obtained in the absence of any clinical observations.Conclusions:We show how variant-specific features can inform a prior probability of disease for rare variants even in the absence of clinically phenotyped heterozygotes.

中文翻译：

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估计罕见 KCNH2 变异的长 QT 综合征诊断的后测概率

背景：遗传分析的激增揭示了遗传变异与疾病之间的许多关联。然而，在大部分健康人群中进行的大规模表型分析工作，加上 DNA 测序，表明目前注释为致病性的变异在健康人群中比以前认为的更常见。此外，在健康个体和疑似疾病患者中，经常在与疾病相关的基因中观察到新的和罕见的变异。这就提出了这些变异是否可以作为疾病的有用预测因子的问题。为了回答这个问题，我们评估了心脏钾通道基因KCNH2 中变异的存在程度是为常染色体显性长QT syndrome.Methods诊断预测：我们估计给定的每个的存在下，长QT诊断的概率KCNH2使用贝叶斯方法的变体，纳入变体的功能，如在变体功能的变化，蛋白质的结构，和在计算机芯片上预测. 我们称这种估计为疾病的后测概率。我们的方法应用于超过 4000 个人杂合的KCNH2 中的871 个错义或框内插入/删除变体并针对 266 个错义或框内插入/缺失变异的 933 个人杂合子的单独国际队列进行了验证。结果：我们的方法对被诊断为长 QT 综合征的杂合子的观察分数进行了很好的校准。启发式地，我们发现从 3 维变异位置、体外功能数据和计算机预测因子中了解一种变异的先天诊断信息等同于通过临床表型对 10 个杂合子了解相同变异的诊断信息。最重要的是，这些数据可以在没有任何临床观察的情况下获得。结论：我们展示了即使在没有临床表型杂合子的情况下，变异特异性特征也可以告知罕见变异的先验概率。