Quantifying and Characterizing the shared genetic contribution to common cancers

 描述(申请人提供)：在美国，乳腺癌、结直肠癌、肺癌、卵巢癌和前列腺癌每年共导致超过85万例癌症诊断和29万例死亡。基于家庭和人群的研究表明，这些癌症具有可遗传成分，而全基因组关联研究(Gwas)确定的多癌易感区域表明，这种遗传性在癌症中部分共享。此外，Gwas还揭示了特定肿瘤亚型(例如雌激素受体(ER)+与ER-乳腺癌)的不同易感区域，为肿瘤侵袭性和死亡率在癌症发病率方面的种族差异提供了一些见解。这项建议将利用OncoArray的数据来研究基因对癌症风险和预后的贡献。这个游戏联盟最近发起了OncoArray倡议，目标是确定乳腺癌、结直肠癌、肺癌、卵巢癌和前列腺癌的新易感基因。总体而言，全球气候变化 这五种癌症的超过35万人的数据正在生成中，这为在一个同质派生的数据集中联合研究多种癌症创造了前所未有的机会。我们将量化和功能表征共同的基因对游戏中癌症的贡献，并使用这些信息来精细绘制多癌GWA区的地图。为了实现我们的目标，我们将利用公共可用的数据库(例如ENCODE)从功能上划分共享的基因成分，使我们能够评估功能类别(例如外显子、调节性)对癌症发展的相对重要性。对于乳腺癌和前列腺癌，这两种癌症的发病率和死亡率存在种族差异，我们将评估欧洲人和非裔美国人祖先之间的遗传相关性。从功能上表征不同种族之间的共同遗传性将突出生物机制，并提供对不同种族之间疾病侵袭性差异的见解。我们将精细绘制多癌GWA区的地图，并估计变异特定的因果关系的后验概率。我们的精细作图方法是独特的，因为它联合建模功能a注释和基因-表型关联，同时允许一个区域内的多个因果SNP，如果存在多个因果变体，则极大地提高统计能力。最后，我们将开发统计方法来量化基因对生存时间变化的贡献，并将我们的方法应用于前列腺癌数据集，该数据集有60,000例(9,000例死亡)。多代人的研究表明，癌症预后通常是从父母传给后代的，这表明这是一个遗传因素，但缺乏足够的统计方法和经验数据集排除了对这种情况的正式评估。该项目利用大规模的OncoArray计划，为我们提供足够的统计能力来获得精确的遗传率估计。我们的结果将阐明癌症生物学，确定癌症和癌症亚型之间的新联系，并对旨在了解癌症致病机制的未来研究的重点和设计产生长期影响。

 DESCRIPTION (provided by applicant): Together, breast, colorectal, lung, ovarian and prostate cancer account for more than 850,000 cancer diagnoses and 290,000 deaths annually in the US. Family- and population-based studies have shown that these cancers have a heritable component, and multi-cancer susceptibility regions identified by genome-wide association studies (GWAS), suggests that this heritability is partly shared across cancers. Furthermore, GWAS have also revealed distinct susceptibility regions for specific tumor subtypes (e.g. estrogen receptor (ER)+ vs. ER- breast cancer), offering some insights to ethnic disparities in cancer incidence by tumor aggressiveness and mortality. This proposal will capitalize on the OncoArray data to study the genetic contribution to cancer risk and prognosis. The GAME-ON consortium recently launched the OncoArray initiative with the goal of identifying novel susceptibility loci for breast, colorectal, lung, ovarian and prostate cancer. In total, GWAS data on more than 350,000 individuals across these five GAME-ON cancers is being generated, creating unprecedented opportunities to jointly study multiple cancers in a homogenously derived dataset. We will quantify and functionally characterize the shared genetic contribution to GAME-ON cancers and use this information to fine-map multi-cancer GWAS regions. To accomplish our goals, we will leverage publically available databases (e.g. ENCODE) to functionally partition the shared genetic component, allowing us to assess the relative importance of functional categories (e.g. exonic, regulatory) for cancer development. For breast and prostate cancer, two cancers with ethnic disparities in cancer incidence and mortality, we will assess the genetic correlation between populations of European and African-American ancestry. Functionally characterizing the shared heritability between ethnicities will highlight biological mechanisms and provide insights into differences in disease aggressiveness between ethnicities. We will fine-map multi-cancer GWAS regions and estimate variant-specific posterior probabilities of causality. Our fine-mapping approach is unique in that it jointly models functiona annotations and the genotype-phenotype association while allowing for multiple causal SNPs within a region, greatly improving statistical power if more than one causal variant exist. Finally we will develop statistical methods to quantify the genetic contribution to variation in survival times and apply our methods on a prostate cancer dataset of 60,000 cases (9,000 deaths). Multi-generational studies show that a cancer prognosis is often "inherited" from parent to offspring suggesting a genetic component but lack of adequate statistical methods and empirical datasets have precluded formal assessment of such. This project capitalizes on the large-scale OncoArray initiative, giving us adequate statistical power to obtain precise heritability estimates Our results will elucidate cancer biology, identify novel connections between cancers and cancer subtypes and have long-standing impact on focus and design of futures studies aiming at understanding the mechanisms causing cancer.