Leveraging the Hidden Genome to Recover the Missing Heritability of Cancer

项目概要 数十年对癌症遗传流行病学的研究已经带来了关于遗传性的更多知识 的疾病。然而，研究结果主要集中在相对少数已知的罕见变异上。 癌症风险基因，通过基因组中的常见变异捕获了适量的额外遗传力 广泛的关联研究。有证据表明，还有很多缺失的遗传力有待解决。 发现了。近年来，新一代测序技术的进步扩大了可检测范围 变异和大规模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”，其中包含与癌症未知相关的区域。隐藏的基因组是 以罕见变异为主，评估它们对癌症风险的个体影响是一项重大挑战 统计功效方面。为了应对这一挑战，我们的团队开发了方法来系统地 根据上下文聚合变体。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以揭示缺失的遗传性。 本研究的目的是 1) 估计可通过以下方式解释的额外遗传力： 隐藏基因组与已知风险变异进行比较，2) 估计不同基因组之间的共享遗传力 癌症类型。使用来自多个来源（包括英国）的全外显子组和全基因组测序数据 生物银行、癌症基因组图谱、全基因组泛癌症分析联盟、美国国立卫生研究院 (NIH) 我们的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，总结 隐藏基因组的信息，以及基于已知风险变异的基准模型。我们会 通过接受者操作特征曲线下的面积评估模型的辨别准确性 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。至 量化不同癌症类型之间共有遗传易感性的程度，我们将计算 来自相应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 遗传力以及与之前双胞胎和家庭研究结果的相关性估计。

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.