DESIGN AND ANALYSIS OF GENOME-WIDE ASSOCIATION STUDIES

描述（由申请人提供）：大约十年前，Risch和Merikangas提出了进行全基因组关联扫描的可能性。虽然当时的成本令人望而却步，但他们预测这些技术障碍最终会被克服。随着50万芯片或芯片技术的出现，每个基因型的成本约为0.2美分，这一预测现在已经成为现实。尽管如此，进行这些研究仍然是昂贵的，并且在有效和有效地设计这些研究方面仍然存在许多方法上的挑战。为了解决这些问题，我们于2005年4月在南加州大学召集了一个由165名来自世界各地的调查人员组成的小组。这些讨论强调了一些研究设计和统计分析问题，我们建议将这些问题作为本合作协议的一部分继续努力。我们的团队还参与了一些针对乳腺癌、结肠癌、前列腺癌和年龄相关性黄斑变性等疾病的研究。我们预计这项研究将为这些研究的开展提供信息，并受到这些项目（以及其他机构的许多其他项目）需求的推动。特别是，我们建议关注以下方法学问题：(1)标签SNP选择和基于单倍型的方法，包括病例-对照关联和病例-病例共享比较；(2)多阶段采样设计的多重测试程序，包括优先考虑snp的分层模型，以进一步考虑使用外部基因组数据；(3)基于家庭和人口的研究，并允许人口分层和混合；(4)基因与基因、基因与环境的相互作用。为了研究这些问题，我们将把这些方法应用到我们自己的研究（多种族队列研究和洛杉矶拉丁裔人眼年龄相关性黄斑变性研究）的真实数据，以及公共数据库（如HapMap Project）中的数据。由于大多数全基因组数据集仅限于相对较小的样本，并且与任何表型信息无关，因此我们将开发方法，使用这些真实数据来生成包含实际程度的遗传多样性的大种群，这些遗传多样性看起来就像在这些小样本中看到的那样。然后，我们将从这些人群中抽取样本，在已知表型模型下模拟重复病例对照数据集，以调查替代研究设计和分析方法的统计性能。

DESCRIPTION (provided by applicant): Nearly a decade ago, Risch and Merikangas suggested the possibility of conducting genome-wide association scans. Although the cost was prohibitive at the time, they predicted that these technological barriers would eventually be overcome. With the advent of 500K chip-based or bead technologies, at a cost of about 0.2 cents per genotype, that prediction has now become a reality. Nevertheless, these will still be expensive studies to conduct and there remain numerous methodological challenges to efficient and valid design of such studies. To address these issues, we convened a panel of 165 investigators from around the world at USC in April 2005. These discussions highlighted a number of study design and statistical analysis problems that we propose to continue working on as part of this Cooperative Agreement. Our team is also involved in conducting and planning several such studies for such conditions as breast, colon, and prostate cancer and age-related macular degeneration. We anticipate that this research will inform the conduct of these studies and be motivated by the needs of these projects (as well as the many others at other institutions). In particular, we propose to focus on the following methodological issues: (1) tag SNP selection and haplotype-based methods incorporating both case-control association and case-case sharing comparisons; (2) multiple testing procedures for multistage sampling designs, including hierarchical models for prioritizing SNPs for further consideration using external genomic data; (3) family- vs. population-based studies and allowance for population stratification and admixture; and (4) gene-gene and gene-environment interactions. To investigate these problems, we will apply the methods to real data from our own studies (the Multiethnic Cohort and the Los Angeles Latino Eye Study of age-related macular degeneration), as well as data available in public databases such as the HapMap Project. Since most genome-wide datasets are limited to relatively small samples and are not connected to any phenotype information, we will develop ways for using these real data to generate large populations that would contain realistic degrees of genetic diversity that would look like those seen in these small samples. We will then sample from these populations to simulate replicate case-control data sets under known phenotype models to investigate the statistical performance of alternative study designs and analysis methods.