Statistical Models for Genetic Studies, Using Network and Integrative Analysis

使用网络和综合分析的遗传研究统计模型

• 批准号: 10134596
• 负责人: Dongjun Chung
• 金额: $ 25.79万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-21 至 2021-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10134596
• 关键词: Statistical; Models; Genetic; Studies; Using

Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which in some cases have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. However, investigation of complex traits often suffers from limited statistical power due to polygenicity, high dimensionality, and moderate sample size. While it is practically challenging and costly to recruit patients to attain sufficient sample size to identify all associated genetic variants, we recently showed that statistical power to identify risk associated genetic variants can be significantly increased by 1) considering genetic basis shared among multiple phenotypes, namely pleiotropy, and 2) incorporating genomic and genetic annotation data. However, effective integration of these datasets becomes statistically more challenging as the number of genetic studies and annotation data increases. The objective of this proposal is to develop statistical methods and software to improve identification and interpretation of risk variants and to promote understanding of genetic relationship among phenotypes. This objective will be attained by pursuing four specific aims. In Aim 1, we will develop a Bayesian graphical model to identify risk variants and construct a phenotype network, by integrating multiple GWAS datasets with various annotation data. In Aim 2, we will develop a Bayesian graphical model to build a phenotype network from biomedical literature. In Aim 3, we will develop a statistical method to construct meta-annotations that can effectively summarize high dimensional annotation data without losing interpretability. In Aim 4, we will apply these methods to genetic studies of vascular complications and autoimmune diseases in African American populations, with PubMed literature and various annotation datasets. The proposed research is innovative because it proposes a novel statistical framework that integrates multiple GWAS, biomedical literature, and annotation datasets to improve identification and interpretation of risk variants. The proposed research is significant because it is expected to help improve diagnosis and treatment of diseases with more effective identification of risk variants and enhanced understanding of common etiology among diseases.

全基因组关联研究（GWAS）已经确定了数以万计的遗传变异 具有数百种表型和疾病，在某些情况下可以提供临床和医疗益处 针对具有新型生物标志物和治疗靶标的患者。但是，经常对复杂性状进行调查 由于多基因，高维和中等样本量，统计功率有限。尽管 招募患者以获得足够的样本量以识别所有相关的样本量实际上具有挑战性和昂贵 遗传变异，我们最近表明，识别风险相关的遗传变异的统计能力可以是 显着增加1）考虑多种表型之间共享遗传基础，即多效性， 2）结合基因组和遗传注释数据。但是，有效整合了这些数据集 随着遗传研究的数量和注释数据的增加，从统计学上变得更具挑战性。 该建议的目的是开发统计方法和软件，以改善身份证明和 对风险变异的解释并促进对表型之间遗传关系的理解。这 追求四个特定目标将实现目标。在AIM 1中，我们将开发贝叶斯图形模型 通过将多个GWAS数据集集成到各种 注释数据。在AIM 2中，我们将开发一个贝叶斯图形模型，以从 生物医学文学。在AIM 3中，我们将开发一种统计方法来构建可以 有效地总结高维注释数据而不会失去可解释性。在AIM 4中，我们将申请 这些方法用于非裔美国人血管并发症和自身免疫性疾病的遗传研究 人群，具有PubMed文献和各种注释数据集。拟议的研究是创新的 因为它提出了一个新颖的统计框架，该框架整合了多个GWA，生物医学文献和 注释数据集以改善风险变体的识别和解释。拟议的研究是 重要的是因为有望帮助改善更有效的疾病的诊断和治疗 鉴定风险变异和对疾病中常见病因的了解增强。