PDF(Pubmed)
Abstract：
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.
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.
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.
We show how variant-specific features can inform a prior probability of disease for rare variants even in the absence of clinically phenotyped heterozygotes.
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.
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.
We show how variant-specific features can inform a prior probability of disease for rare variants even in the absence of clinically phenotyped heterozygotes.
摘要：
遗传谱分析的增殖揭示了遗传变异与疾病之间的许多关联。然而,在很大程度上健康的人群中进行大规模的表型鉴定工作，再加上DNA测序,表明目前被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,在健康个体和怀疑疾病的个体中，与疾病相关的基因中经常观察到新的和罕见的变异。这提出了这些变体是否可以是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变异的存在对常染色体显性遗传长QT综合征的诊断预测程度.
我们估计了长QT诊断的概率，考虑到每个KCNH2变异的存在，使用贝叶斯方法，结合变异特征，如变异功能的变化，蛋白质结构，和硅预测。我们称这种估计为疾病的后验概率。我们的方法适用于KCNH2中871个错义或框内插入/缺失变体的超过4000个个体杂合，并针对一个单独的933个个体的国际队列验证了266个错义或框内插入/缺失变体的杂合。
我们的方法对诊断为长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,我们发现从三维变异位置了解变异的先天诊断信息，体外功能数据，计算机预测因子相当于通过临床表型分析10个杂合子来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.
我们展示了即使在没有临床表型杂合子的情况下，变体特异性特征如何为罕见变体提供疾病的先验概率。
我们估计了长QT诊断的概率，考虑到每个KCNH2变异的存在，使用贝叶斯方法，结合变异特征，如变异功能的变化，蛋白质结构，和硅预测。我们称这种估计为疾病的后验概率。我们的方法适用于KCNH2中871个错义或框内插入/缺失变体的超过4000个个体杂合，并针对一个单独的933个个体的国际队列验证了266个错义或框内插入/缺失变体的杂合。
我们的方法对诊断为长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,我们发现从三维变异位置了解变异的先天诊断信息，体外功能数据，计算机预测因子相当于通过临床表型分析10个杂合子来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.
我们展示了即使在没有临床表型杂合子的情况下，变体特异性特征如何为罕见变体提供疾病的先验概率。
