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

• 批准号: 9068954
• 负责人: Wesley Kurt Thompson
• 金额: $ 40.44万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 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/9068954
• 关键词: Accounting; Address; Architecture; Bioconductor; Blood Pressure; Cholesterol; Chromosome Mapping; Complex; Computer software; Crohn' 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

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)：正如最近所说的，“到目前为止，Gwas只确定了常见疾病遗传性的一小部分，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”被归因于许多潜在的原因，而且很明显，大多数复杂的性状都受到许多基因的影响，每个基因的影响都太小，无法用传统的GWAS数据分析来可靠地发现。我们建议开发几种创新的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从当前GWA得出的数据中检测真实(非空)效应的能力。虽然我们强调应用于目前已有的用于炎症性肠病和心血管疾病风险因素的GWAS数据，但同样的方法学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：制定纳入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，利用我们小组最近开发的LD加权SNP注释方法，扩展当前对单变量表型GWA场状态的分析。具体地说，我们建议扩展混合模型方法来解释SNP LD加权的函数注释。目标2：开发纳入多因素关系的统计方法，以提高发现率。我们将推广混合模型方法，以同时包含来自两个表型的SNP的z分数之间的协方差(即多效性)，并使用未覆盖的多效性关系来提高SNP发现和复制的能力。目的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。我们将使用多效性经验贝叶斯分析的后验效应大小估计作为输入，以解释表型的共同和独特的遗传机制，以及分子途径。目标4：开发和分发软件。实施AIMS 1-3中开发的方法的计算机软件将作为免费提供和用户友好的R包分发，托管在BioConductor.org上，并作为一套交互的基于图形用户界面的程序在我们实验室托管的网站上提供。