Clinical prioritization of reported disease variants in asymptomatic individuals

描述（由申请人提供）：全基因组测序（WGS）具有改善医疗保健的潜力，但仍需做出大量努力将序列数据转化为有意义的临床解释。WGS的解释必须解决新观察到的可能有害的遗传变异，以及对医学和科学文献中已报告与疾病相关的150，000多个变异的审查。这些发现中有许多是在小型队列和案例研究中发现的，因此很难将其转化为 携带这些变异的无症状个体的疾病风险。如果没有对这些关联的准确风险估计，我们可能会使健康患者暴露于假阳性结果，导致不必要的诊断检查和筛查，这将大大增加医疗成本和患者发病率。 WGS解释的核心是开发一种标准化的方法来过滤可能的良性结果，并优先考虑那些可能 具有临床意义和科学有效性。虽然这些先前确定的变量中的许多与个别罕见的孟德尔疾病（例如，肥厚性心肌病和神经纤维瘤病）相关，但这些疾病是共同常见的，形成了为许多个体赋予疾病风险的长尾。由于这些疾病都非常罕见，很难想象一种专门的解释方法来计算每种疾病的风险，因此我们提出了一种广泛适用于许多罕见疾病的系统方法来评估变异疾病风险。 为了满足这一迫切需求，我们将开发一种新的方法，该方法使用每种疾病的先验概率以及受影响和未受影响个体的该疾病的所有已知遗传变异的群体频率来估计疾病相关变异的发生率。这种疾病的先验概率被测量为患病率，或受疾病影响的人群中的个体比例。由于孟德尔疾病的患病率实际上是其所有遗传变异（以及其他行为和环境因素）的遗传率和频率的组合，因此我们建议使用疾病患病率和相关变异的分布来估计这些遗传率值。如果只有一种变异与疾病相关，该疾病的总发病率和人群频率应与疾病流行率密切相关，但如果有许多疾病相关变异，每种变异对疾病的总体负担的贡献较小，并根据其在人群中的频率进行调整。然后，我们将使用这些突变率估计来建立可能的良性变异的全基因组过滤截止值，并优先考虑观察到的WGS变异，供临床遗传学家审查。然后，我们建议使用这些值来过滤和排名在现有的临床试验中观察到的个体WGS数据集的变化，并将这些与现有的临床遗传学解释进行比较。

DESCRIPTION (provided by applicant): Whole genome sequencing (WGS) has the potential to improve medical care, but much effort remains to translate sequence data into meaningful clinical interpretations. WGS interpretation must address both newly observed genetic variants that are likely to be harmful, as well as the review of over 150,000 variants that are already reported to be associated with disease from the medical and scientific literature. Many of these discoveries were made in small cohort and case studies, making it difficult to translate these into disease risks for asymptomatic individuals that carry these variants. Without accurate risk estimates for these associations, we may potentially expose healthy patients to false positive findings, leading to needless diagnostic workups and screenings that will substantially increase medical costs and patient morbidity. Central to WGS interpretation is the development of a standardized methodology to filter likely benign results, and to prioritize those variants that may be clinically significant and scientifically valid. While many of these previously identified variats are associated with Mendelian disorders that are individually rare, (e.g. hypertrophic cardiomyopathy and neurofibromatosis,) these disorders are collectively common, forming a long tail that confers disease risk for many individuals. Because each of these diseases is so rare, it is hard to envision a specialized interpretive approach to calculate risk for each disease so we propose a systematic approach that is broadly applicable across many rare diseases to assess variant disease risk. To meet this urgent need, we will develop a novel approach that estimates the penetrance of disease- associated variants using the prior probability of each disease, and the population frequencies of all of the known genetic variants for that disease for affected and unaffected individuals. This prior probability of disease is measured as the prevalence, or the proportion of individuals in a population affected with a disorder. Because the prevalence of a Mendelian disease is actually a combination of the penetrance and frequency of all of its genetic variation (as well as other behavioral and environmental factors) we propose to estimate these penetrance values using the disease prevalence and distribution of associated variation, for each disease. If there is only one variant associated with a disease, the total penetrance and population frequency for that disease should be closely correlated with disease prevalence, but if there are many disease-associated variants, each contributes less to the overall burden of diseases, adjusted by its frequency in the population. We will then use these penetrance estimates to establish genome-wide filtering cutoffs for likely benign variation and to prioritize observed WGS variation for review by clinical geneticists. We then propose to use these values to filter and rank the observed variation in individual WGS datasets in an existing clinical trial, and to compare these with existing clinical genetics interpretations.