DESIGN AND ANALYSIS OF GENOME-WIDE ASSOCIATION STUDIES

描述(申请人提供)：大约十年前，Risch和Merikangas建议进行全基因组关联扫描的可能性。尽管当时的成本高得令人望而却步，但他们预测，这些技术壁垒最终将被克服。随着500K芯片或珠子技术的出现，每个基因的成本约为0.2美分，这一预测现在已经成为现实。尽管如此，进行这些研究仍将是昂贵的，而且在有效和有效地设计这些研究方面仍然存在许多方法学挑战。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来自世界各地的165名调查人员组成的小组。这些讨论突出了一些研究设计和统计分析问题，我们建议作为这项合作协定的一部分继续开展工作。我们的团队还参与了几项针对乳腺癌、结肠癌、前列腺癌和老年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)的需要的推动。特别是，我们建议重点关注以下方法论问题：(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-病例分享比较；(2)多阶段抽样设计的多重测试程序，包括利用外部基因组数据对SNP进行优先排序的分层模型；(3)基于家庭和基于人群的研究，以及允许种群分层和混合；以及(4)基因-基因和基因-环境相互作用。为了调查这些问题，我们将把这些方法应用于我们自己的研究(多种族队列和洛杉矶老年性黄斑变性拉丁眼研究)的真实数据，以及HapMap项目等公共数据库中的数据。由于大多数基因组范围的数据集仅限于相对较小的样本，并且与任何表型信息没有联系，我们将开发出使用这些真实数据来生成包含实际遗传多样性程度的大种群的方法，这些遗传多样性看起来就像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复制病例对照数据集，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.