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到目前为止只确定了一小部分常见疾病的遗传性，因此做出有意义的预测的能力仍然相当有限”（Collins, 2010）。这种“缺失的遗传性”被归因于许多潜在的原因，而且很明显，大多数复杂的性状受到许多基因的影响，每个基因的影响都太小，无法使用传统的GWAS数据分析来可靠地发现。我们建议开发几种创新方法来增强基因发现，提高预测模型的复制率和泛化性能。这些方法将大大提高检测当前GWAS数据中真实（非空）效应的能力。虽然我们强调将现有的GWAS数据应用于炎症性肠病和心血管疾病风险因素，但同样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：开发包含功能注释的统计方法，以提高发现率。我们将开发和实施方法，使用我们小组最近开发的ld加权SNP注释方法，扩展当前单变量表型GWAS的现场分析。具体来说，我们建议扩展混合模型方法来考虑SNP ld加权的功能注释。目标2：发展包含多效关系的统计方法以提高发现率。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），并利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作为功能表征和途径分析的先验。我们将使用多效贝叶斯经验分析的后验效应大小估计作为输入，以解释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。计算机软件，实现目标1-3中开发的方法，将作为一个免费的、用户友好的R包在bioconductor网站上发布，并作为一套基于交互式gui的程序在我们实验室托管的网站上发布。

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.