AMD genetics: methods and analysis for progression, prediction, and association

描述(申请人提供)：老年性黄斑变性(AMD)是西方国家老年人口失明的主要原因。在过去的几年里，通过全基因组关联研究(GWAS)，通过个体研究或通过对国家眼科研究所(NEI)支持的AMD基因联盟的多项研究的荟萃分析，已经确定了十几个AMD风险基因座。一项正在对38,000名AMD/对照受试者进行的Exome芯片实验将通过发现更多的稀有变体来进一步扩大名单。然而，分析和统计方法仍然落后于数据生成的步伐。来自我们的合作者、AMD Exome芯片联盟和公共数据库(例如，DBGaP)的新出现的遗传和表型数据将使我们能够测试新的假设，开发和校准统计方法，以促进我们参与的正在进行的联盟研究。特别是，我们感兴趣的是系统地研究AMD进展的遗传原因和预测，识别非裔美国人队列中的疾病易感基因，并为具有二元特征的基于家庭的研究开发关联方法。为了实现这些目标，我们提出的具体目标如下：1)开发一个双变量生存框架来联合模拟双眼AMD的进展，并使用来自AREDS(年龄相关眼病研究)、AREDS2和密歇根大学进行的AMD研究的4000多个合格样本进行AMD进展的全基因组关联研究；2)开发和验证严格的统计模型，用于基于目标1的结果的人口统计学、临床和遗传信息预测AMD的发生和进展，并获得考虑到不同研究设计和双眼之间的相关性的预测概率；3)开发和应用新的方法，结合来自关联和混合作图的信号，在725名无关的非裔美国人中识别与AMD风险相关的基因座；以及4)利用功能建模方法，在广义线性混合模型框架下，开发一种家系中二元性状罕见变异关联检验的统计方法，并将该方法应用于我们基于加州大学洛杉矶分校-匹兹堡分校的2,188个样本的研究。我们的研究结果将促进我们对AMD的发病机制和预防的认识。我们开发和应用的方法将可用于其他研究小组，并将有助于正在进行的AMD联盟数据的分析。此外，我们的方法也可以应用到其他视觉研究中。我们研究团队的独特优势包括：在AMD数据集应用分析方面的丰富经验，杰出的统计遗传学专业知识，以及对他们收集的AMD数据集具有深刻洞察力的临床顾问。成功完成我们的目标，我们将开发和应用最先进的统计方法，将丰富我们对AMD发病机制的理解，改善个人风险预测，因此将有助于提高临床实践。

DESCRIPTION (provided by applicant): Age-related macular degeneration (AMD) is a leading cause of blindness in the elderly population of Western countries. In the past few years, over one dozen AMD risk loci have been identified through genome-wide association studies (GWAS), either by individual studies or through meta-analyses of multiple studies from the National Eye Institute (NEI) supported AMD Gene Consortium. An ongoing exome chip experiment on 38,000 AMD/Control subjects will further expand the list by discovering additional rare variants. However, the analyses and statistical methods are still lagging behind the pace of data generation. Emerging genetic and phenotypic data from our collaborators, the AMD Exome Chip Consortium, and public databases (e.g. the dbGaP) will allow us to test new hypotheses, develop and calibrate statistical methods to facilitate ongoing consortium studies in which we are involved. In particular, we are interested in systematically studying the genetic causes and prediction of AMD progression, identifying disease-susceptibility loci in a cohort of African Americans, and developing association methods for family-based studies with binary traits. To achieve these goals, we propose specific aims as follows: 1) To develop a bivariate survival framework to jointly model AMD progression in both eyes and to perform a genome-wide association study of AMD progression using over 4,000 eligible samples from AREDS (Age-Related Eye Disease Study), AREDS2, and the AMD study conducted at the University of Michigan; 2) To develop and validate rigorous statistical models for prediction of AMD occurrence and progression based on demographic, clinical, and genetic information from the results of Aim 1 and to obtain predictive probabilities accounting for different study designs and the correlation between two eyes; 3) To develop and apply novel methods to identify loci associated with AMD risk in 725 unrelated African Americans, combining signals from both association and admixture mapping; and 4) To develop a statistical method for rare variant association tests of binary traits in families under the framework of generalized linear mixed model using a functional modeling approach and to apply the method to our UCLA- Pittsburgh family-based study of 2,188 samples. Our results will advance our understanding of pathogenesis and prevention of AMD occurrence and its progression. The methods we developed and applied will be available to other study groups and will benefit the analysis of ongoing AMD consortium data. In addition, our methods can be applied to other vision research as well. Unique strengths of our research team include: extensive prior experience in the applied analyses of AMD data sets, outstanding statistical genetics expertise, and clinical consultants with deep insight into the AMD data sets they collected. Successful completion of our Aims, where we will develop and apply state-of-the-art statistical methods, will enrich our understanding of AMD pathogenesis and improve individual risk prediction, and therefore will help enhance clinical practice.