Are shared controls useful in genome-wide association studies for cancer genetic

描述（由申请人提供）： 最近，从全基因组关联研究（GWAS）中收集了对常见癌症遗传流行病学的深刻见解。这一见解有可能导致未来对癌症风险的预测性基因检测，并发现新的生物靶点进行治疗干预。进行此类研究的困难包括基因分型成本、在医院环境中识别对照的问题以及此类研究所需的大样本量。为了解决这些问题，已经提出了在GWAS中对常见疾病使用共享的共同控制。虽然在流行病学上不严格，但这种方法可能具有几个实际优势。在这里，这种方法的实用性将通过严格测试的假设，即使用共享控制可以提高癌症遗传流行病学的全基因组关联研究的力量。无论这项研究的结果是否会导致接受或拒绝这一假设，其结果将有助于规划未来的癌症遗传流行病学全基因组关联研究。该假设将通过实现以下目标进行检验：1）比较使用共享对照与传统病例对照研究设计的功效; 2）确定解释和适当控制病例和对照人群中不同人群亚结构的最佳方法; 3）检验共享对照识别一组可能与胰腺癌相关的SNP的能力。将使用分析把握度计算和经验模拟来确定共享对照设计与经典病例对照研究相比的表现。共享控制的适当数量，环境协变量的影响，在共享控制中使用插补，以及控制人群分层的各种方法都将被检查。现有的胰腺癌GWAS数据集将与CGEMS研究的共享对照进行检查，以提供这种方法的真实世界示例。 公共卫生相关性： 全基因组关联研究是了解包括癌症在内的常见疾病遗传学的强大而昂贵的工具。在这里，我们将严格测试一种方法，通过结合许多此类研究中健康个体的数据来改进这些研究。我们将在胰腺癌研究中验证这种方法，它可以在不大幅增加成本的情况下实现更强大的全基因组关联研究。

DESCRIPTION (provided by applicant): Project Summary Recently, great insight into the genetic epidemiology of common cancers has been gleaned from genome-wide association studies (GWAS). This insight has the potential to lead to future predictive genetic testing for cancer risk and the discovery of new biological targets for therapeutic intervention. Difficulties in performing such studies include genotyping cost, problems in identifying controls in a hospital-based setting, and the large sample sizes required for such studies. To obviate these problems, the use of shared, common controls in GWAS for common diseases has been proposed. While not epidemiologically rigorous, such an approach could have several practical advantages. Here, the utility of this approach will be examined by rigorously testing the hypothesis that the use of shared controls can boost the power of genome-wide association studies in cancer genetic epidemiology. No matter whether findings from this research lead to acceptance or rejection of this hypothesis, the results will be useful for planning future genome-wide association studies in cancer genetic epidemiology. This hypothesis will be tested through achievement of the following aims: 1) compare the power of using shared controls with a traditional case-control study design; 2) determine the optimal approach to account for and properly control for different population substructure in the case and control populations; and 3) test the ability of shared controls to identify a set of SNPs potentially associated with pancreatic cancer. Analytical power calculations and empirical simulations will be used to determine how well a shared control design will perform compared to a classical case-control study. The appropriate number of shared controls, the influence of environmental covariates, the use of imputation in the shared controls, and various methods for controlling population stratification will all be examined. An existing pancreatic cancer GWAS data set will be examined with shared controls from the CGEMS study to provide a real-world example of this approach. PUBLIC HEALTH RELEVANCE: Relevance Genome wide association studies are a powerful and expensive tool to understand the genetics of common diseases including cancer. Here, we will rigorously test an approach to improve these studies by combining data from healthy individuals in many such studies. This approach, which we will validate on a study of pancreatic cancer, could enable more powerful genome-wide association studies without a vast increase in cost.