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

多基因性、多效性和功效：基因发现的新统计方法

基本信息

批准号：
8858642
负责人：
Wesley Kurt Thompson
金额：
$ 40.84万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-06-05 至 2018-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8858642
关键词：
Accounting Address Architecture Bioconductor Blood Pressure Cholesterol Chromosome Mapping Complex Computer software Crohn&apos s disease Data Data Analyses Data Set Databases Development Diastolic blood pressure Disease European Genes Genetic Genome Genotype Goals Health Heritability High Density Lipoproteins Human Individual Inflammatory Bowel Diseases Investigation Methodology Methods Modeling Molecular Outcome Study Output Pathway Analysis Pathway interactions Performance Phenotype Public Health Publishing Risk Risk Factors Sampling Statistical Methods Testing Triglycerides Ulcerative Colitis Weight base blood lipid cardiovascular disorder risk deep sequencing gene discovery genetic association genome wide association study improved innovation method development next generation sequencing novel pleiotropism predictive modeling programs simulation statistics tool trait user-friendly web site

项目摘要

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.

描述（由申请人提供）：正如最近所述，“迄今为止，GWAS 仅识别出常见疾病遗传力的一小部分，因此做出有意义的预测的能力仍然相当有限”（Collins，2010）。这种“遗传性缺失”可归因于许多潜在原因，而且很明显，大多数复杂性状都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 数据分析可靠地发现。我们建议开发几种创新方法来增强基因发现并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当前 GWAS 数据中真实（非零）效应的能力。虽然我们强调当前炎症性肠病和心血管疾病危险因素的 GWAS 数据的应用，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：开发结合功能注释的统计方法，以提高发现率。我们将使用我们小组最近开发的 LD 加权 SNP 注释方法来开发和实施扩展单变量表型 GWAS 的当前现状分析的方法。具体来说，我们建议扩展混合模型方法来解释 SNP LD 加权功能注释。目标 2：开发结合多效关系的统计方法，提高发现率。我们将推广混合模型方法，以同时包含来自两个表型的 SNP 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作为功能表征和路径分析的先验。我们将使用多效性经验贝叶斯分析的后验效应大小估计作为输入，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 Bioconductor.org 上托管的免费且用户友好的 R 包进行分发，并在我们实验室托管的网站上作为一套基于 GUI 的交互式程序进行分发。