Quantifying and Characterizing the shared genetic contribution to common cancers

 描述（由申请人提供）：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850，000例癌症诊断和290，000例死亡。基于家庭和人群的研究表明，这些癌症具有遗传成分，全基因组关联研究（GWAS）确定的多癌症易感区域表明，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的癌症发病率的种族差异提供了一些见解。该提案将利用OncoArray数据来研究遗传对癌症风险和预后的贡献。GAME-ON联盟最近启动了OncoArray计划，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座。GWAS共计 正在生成这五种GAME-ON癌症中超过35万人的数据，为在同质衍生数据集中联合研究多种癌症创造了前所未有的机会。我们将对GAME-ON癌症的共享遗传贡献进行量化和功能表征，并使用这些信息来精细绘制多癌症GWAS区域。为了实现我们的目标，我们将利用化学上可用的数据库（例如ENCODE）来功能性地划分共享的遗传组分，使我们能够评估功能类别（例如外显子，调节）对癌症发展的相对重要性。对于乳腺癌和前列腺癌这两种在癌症发病率和死亡率方面存在种族差异的癌症，我们将评估欧洲和非洲裔美国人血统人群之间的遗传相关性。对种族间共享遗传力的功能表征将突出生物学机制，并提供对种族间疾病侵袭性差异的见解。我们将精细映射多癌症GWAS区域，并估计变量特异性因果关系的后验概率。我们的精细映射方法是独特的，因为它联合模型功能注释和基因型-表型关联，同时允许在一个区域内的多个因果SNP，大大提高统计能力，如果一个以上的因果变异存在。最后，我们将开发统计方法来量化遗传对生存时间变化的贡献，并将我们的方法应用于60，000例（9，000例死亡）的前列腺癌数据集。多代研究表明，癌症预后往往是从父母“遗传”给后代，这表明遗传成分，但缺乏足够的统计方法和经验数据集，排除了正式的评估。该项目利用了大规模OncoArray计划，为我们提供了足够的统计能力来获得精确的遗传性估计，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，并对未来研究的重点和设计产生长期影响，旨在了解导致癌症的机制。

 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.