A major challenge emerging in genomic medicine is how to assess best disease risk from rare or novel variants found in disease-related genes. The expanding volume of data generated by very large phenotyping efforts coupled to DNA sequence data presents an opportunity to reinterpret genetic liability of disease risk. Here we propose a framework to estimate the probability of disease given the presence of a genetic variant conditioned on features of that variant. We refer to this as the penetrance; the fraction of all variant heterozygotes that will present with disease. We demonstrate this methodology using a well-established disease-gene pair, the cardiac sodium channel gene SCN5A and the heart arrhythmia Brugada syndrome. From a review of 756 publications, we developed a pattern mixture algorithm, based on a Bayesian Beta-Binomial model, to generate SCN5A penetrance probabilities for the Brugada syndrome conditioned on variant-specific attributes. These probabilities are determined from variant-specific features (e.g. function, structural context, and sequence conservation) and from observations of affected and unaffected heterozygotes. Variant functional perturbation and structural context prove most predictive of Brugada syndrome penetrance.

基因组医学中出现的一个主要挑战是如何评估疾病相关基因中发现的罕见或新颖变异的最佳疾病风险。大规模表型分析工作与 DNA 序列数据相结合产生的数据量不断扩大，为重新解释疾病风险的遗传责任提供了机会。在这里，我们提出了一个框架来估计疾病的概率，因为存在以该变异特征为条件的遗传变异。我们将此称为外显率；出现疾病的所有变异杂合子的比例。我们使用成熟的疾病基因对、心脏钠通道基因SCN5A和心律失常 Brugada 综合征来证明这种方法。通过对 756 篇出版物的回顾，我们开发了一种基于贝叶斯 Beta 二项式模型的模式混合算法，以生成以变异特定属性为条件的 Brugada 综合征的SCN5A外显率概率。这些概率是根据变体特异性特征（例如功能、结构背景和序列保守性）以及对受影响和未受影响的杂合子的观察来确定的。变异的功能扰动和结构背景证明最能预测 Brugada 综合征的外显率。