Polygenicity, Pleiotrophy and Power: Novel Statistical Methods for Gene Discovery

描述（由申请人提供）：正如最近所述，“GWAS迄今为止仅确定了常见疾病遗传性的一小部分，因此做出有意义预测的能力仍然非常有限”（柯林斯，2010）。这种“缺失的遗传性”被归因于许多潜在的原因，并且已经清楚的是，大多数复杂的性状受到许多基因的影响，每个基因的影响太小，以至于无法使用GWAS数据的传统分析可靠地发现。我们建议开发几种创新的方法来增强基因发现，提高预测模型的复制率和泛化性能。这些方法将极大地提高检测来自当前GWAS的数据中的真实（非空）效应的能力。虽然我们强调应用于目前现有的炎症性肠病和心血管疾病风险因素的GWAS数据，但相同的方法框架将适用于下一代测序数据。该提案的具体目标是：目标1：开发统计方法来解释提高发现率的函数注释。我们将开发和实施的方法，扩展目前的国家的领域分析GWAS的单变量表型，使用LD加权SNP注释方法最近开发的我们的小组。具体而言，我们建议扩展混合模型方法，以考虑SNP LD加权功能注释。目标2：开发统计方法，阐明多效关系，提高发现率。我们将推广混合模型方法以涵盖同时来自两种表型的SNP的z分数之间的协方差（即，多效性），并使用未发现的多效性关系来提高SNP发现和复制的能力。目标3：使用经验贝叶斯模型的估计值作为功能表征和通路分析的先验。我们将使用来自多效性经验贝叶斯分析的后验效应量估计作为输入，以阐明表型的共享和独特的遗传机制以及分子途径。目标4：开发和分发软件。实现目标1-3中开发的方法的计算机软件将作为免费提供的用户友好的R包在Bioconductor.org上分发，并作为一套基于GUI的交互式程序在我们实验室托管的网站上提供。

DESCRIPTION (provided by applicant): As recently stated, "GWAS have so far identified only a small fraction of the heritability of common diseases, so the ability to make meaningful predictions is still quite limited" (Collins, 2010). This "missing heritability" has been attribute to a number of potential causes, and it has become clear that most complex traits are influenced by many genes, each with effects too small to be reliably discovered using traditional analyses of GWAS data. We propose to develop several innovative approaches to enhance gene discovery and improve replication rates and generalization performance of predictive models. These methods will vastly increase the power to detect true (non-null) effects in data derived from current GWAS. While we emphasize applications to currently existing GWAS data for Inflammatory Bowel Disease and Cardiovascular Disease Risk Factors, the same methodological framework will be applicable to next generation sequencing data. The Specific Aims of the proposal are: Aim 1: To Develop Statistical Methods Incorporating Functional Annotations that Improve Discovery Rates. We will develop and implement methods that extend current state-of-the-field analyses for GWAS of univariate phenotypes, using the LD-weighted SNP annotation methodology recently developed by our group. Specifically, we propose to extend the mixture model approach to account for SNP LD-weighted functional annotations. Aim 2: To Develop Statistical Methods Incorporating Pleiotropic Relationships that Improve Discovery Rates. We will generalize the mixture model approach to encompass covariance between z-scores of SNPs from two phenotypes simultaneously (i.e., pleiotropy) and to use the uncovered pleipotropic relationships to improve power for SNP discovery and replication. Aim 3: To Use Estimates from Empirical Bayes Models as Priors in Functional Characterization and Pathway Analyses. We will use posterior effect size estimates from pleiotropic Empirical Bayes analyses as inputs to explicate shared and unique genetic mechanisms of phenotypes, as well as molecular pathways. Aim 4: To Develop and Distribute Software. Computer software, implementing the methods developed in Aims 1-3, will be distributed as a freely available and user-friendly R package hosted on Bioconductor.org and as a suite of interactive GUI-based programs available on a website hosted by our lab.