Leveraging the Hidden Genome to Recover the Missing Heritability of Cancer

项目摘要 几十年来对癌症遗传流行病学的研究已经使人们对癌症的遗传性有了更多的了解。 疾病。然而，研究结果主要集中在相对较少的已知基因中的罕见变异上。 癌症风险基因，以及基因组中常见变异所捕获的适量额外遗传性， 广泛的协会研究。有证据表明，有很多缺失的遗传性，仍然是 发现了近年来，下一代测序的进展扩大了可检测的范围。 变异和大规模的全外显子组和全基因组测序工作为 研究一个巨大的"隐藏基因组"，其中包含与癌症相关性未知的区域。隐藏的基因组是 由罕见变异主导，评估它们对癌症风险的个体影响是一个重大挑战， 从统计学的角度来看。为了应对这一挑战，我们的团队开发了一种方法， 根据上下文聚合变量。我们假设该方法可以应用于现有的整体- 外显子组和全基因组测序数据集来揭示缺失的遗传性。 这项研究的目的是：1）估计可由以下因素解释的额外遗传力： 隐藏基因组与已知风险变体的比较，以及2）估计不同风险变体之间的共享遗传力， 癌症类型。使用来自多个来源的全外显子组和全基因组测序数据，包括英国 生物库，癌症基因组图谱，全基因组泛癌分析联盟，美国国立卫生研究院所有的 美国研究计划和其他来源，我们将开发特定地点的癌症风险模型， 隐藏的基因组信息，以及基于已知风险变异的基准模型。我们将 通过受试者工作特征曲线下面积评估模型的判别准确性 并使用先前建立的公式将曲线下面积转化为遗传力的估计值。到 量化不同癌症类型之间共享遗传易感性的程度，我们将计算 来自相应的隐藏基因组模型的预测集之间的相关性。我们将比较我们的 遗传力和相关性估计与以前的研究结果从双胞胎和家庭。

PROJECT SUMMARY Decades of research into the genetic epidemiology of cancer have led to much knowledge about the heritability of the disease. However, findings have largely focused on rare variants in a relatively small number of known cancer risk genes, with modest amounts of additional heritability captured by common variants from genome- wide association studies. Evidence suggests that there is much missing heritability that remains to be discovered. In recent years, advances in next-generation sequencing have widened the range of detectable variants and large-scale whole-exome and whole-genome sequencing efforts have opened opportunities to investigate a vast “hidden genome” comprising regions of unknown relevance to cancer. The hidden genome is dominated by rare variants, and evaluating their individual impact on cancer risk presents a major challenge in terms of statistical power. To address this challenge, our team has developed methodology to systematically aggregate variants based on their context. We hypothesize that the approach can be applied to existing whole- exome and whole-genome sequencing datasets to uncover missing heritability. The aims of the proposed study are 1) to estimate the additional heritability that can be explained by the hidden genome compared to known risk variants and 2) to estimate shared heritability between different cancer types. Using whole-exome and whole-genome sequencing data from multiple sources, including the UK Biobank, The Cancer Genome Atlas, the Pan-Cancer Analysis of Whole Genomes Consortium, the NIH All of Us research program, and other sources, we will develop site-specific cancer risk models that summarize information across the hidden genome, as well as benchmark models based on known risk variants. We will assess the discriminatory accuracy of the models via the area under the receiver operating characteristic curve and translate the areas under the curve into estimates of heritability using a previously established formula. To quantify the extent of shared genetic susceptibility between different cancer types, we will calculate correlations between sets of predictions from the corresponding hidden genome models. We will compare our heritability and correlation estimates with previous findings from twin and family studies.